The potential of emerging technology in the Netherlands Analysis of Dutch deep tech ecosystems’ potential impact to determine winning strategies Published: June 2021

Authored by: Bas van der Starre, Elmar Cloosterman, Dolfine Kosters & Bella Suwarso

Commissioned by: Contents

• Research question & scope p. 4

• Deep tech opportunities p. 8

• Analysis: the current Dutch position in deep tech p. 20

• Analysis: the scope of impact of Dutch deep tech p. 30

• Appendices p. 40 Research question

What are winning strategies to intervene in the deep tech ecosystem?

The Netherlands is one of the most competitive economies. Despite its size it Research questions for winning manages to be 4th on the World Economic Forum competitiveness index. strategies Part of this success is due to the thriving Dutch start-up ecosystem. Its tech Deep tech investments in start-ups have attracted over 1,8 B€ in investments in 2020* and are the 2020 as part of total tech fastest growing job engine in the Netherlands. investments (in M€) A. What are the most promising Deep tech Other An increasing share of start-ups is focused on deep tech. These companies are deep technology-industry areas on the frontier of science and technology, facing great risk and a long runway 100% for the future of the Netherlands to market application. The potential for deep tech start-ups to transform that can add value to the Dutch ecosystem? markets and industries is enormous. Despite this potential, only 15% of 80% investments in the Netherlands (267 M€) in 2020 was in deep technology, lagging behind other European start-up ecosystems.* 60% There is a need for intervention in the Dutch deep tech start-up ecosystem. Public guidance and new resources are required to support the generation and 40% B. What are possible growth of deep tech ventures. But public resources are not unlimited, if these interventions in each technology- are spread too thin across markets and technologies the potential impact of industry area so that public these actions might be too little and private actions might not follow suit. 20% resources are used most 8.900 Dutch deep tech start-ups might lose their transformative opportunity. To 267 effective? make the right choices, we need insight in the most promising technology- 0% industry areas. And we require winning strategies on how to stimulate NL Europe these areas. This leads to our research questions, stated on the right.

Sources: World Economic Forum (2019), The Global Competitiveness Report 2019. Techleap.nl, CBRE & Dealroom (2020), Startup jobs are a growth engine worth strengthening. 2nd edition. 4 September 2020. * The state of Dutch Tech (2021) & Atomico (2021). The state of European Tech 2020. Scope of research

9 key enabling deep technologies across 9 industries and 4 challenges

This research focuses on clusters of deep technologies, coupled to crucial Deep technologies Crucial industries industries in the Netherlands and current societal challenges. • Deep technologies: “Deep-tech are defined as disruptive solutions Augmented & Chemical Industry built around unique, protected or hard-to-reproduce technological or scientific & Data Construction advances.” The companies that engage in deep tech innovation have a strong Blockchain Energy research base and attempt to advance the technological frontier. These nine deep technologies represent (overlapping) key areas of promising technological Internet of Things Food innovation in the coming decade. Logistics • overlaps with several deep technologies but is not made a separate category, see Appendix for rationale. Advanced Materials High Tech Industries ICT • Crucial industries: These are crucial industries for the national economy (in Nanotechnology Health & Pharma terms of jobs, exports, or products that they provide) that at the same time are × faced with international competition, technological innovation and the Quantum Technology Financial services potential to play a role in societal challenges. Deep technologies have the Energy & Sustainability potential to change one or more of these industries. • See appendix for description of each industry. Food & Water

• Societal challenges: These are (international) areas of concern where deep Health & Wellbeing Societal

technologies are expected to play a part in the solution. They are based on challenges Dutch mission driven innovation policy. Safety & Security • See appendix for description of each challenge area.

Technology selection based on BCG & Hello Tomorrow (2018). From Tech to Deep Tech, the Dutch key enabling technology strategy (2019) and European Commission (2020), Advanced 5 Technologies for Industry – General findings. Industry and challenge areas selection based on Dutch Mission driven top industry & innovation policy (2019). See appendices for technology, industry & challenge area selection methodology. Research framework

Our method identifies four variables that are crucial for deep tech success

Current Dutch position Scope of impact

1. Scientific leadership Is the Netherlands at the forefront of 3. Economic potential of technology Does the Netherlands have industrial technology development relative to strength in relevant industries to other nations? provide start-ups with a primary market? Based on scientific quantity, quality, focus and academic spin-offs. Based on jobs, exports, added value and R&D leadership.

2. Start-up activity Is the Netherlands able to start new 4. Societal impact of technology How important is the deep technology deep tech firms that have growth for contributing to solutions in potential and attract investment? (international) societal challenges.

Based on start-up populations for Based on expert expectations on the each technology, growth, investments role of deep technologies in specific and technological readiness. agendas.*

6 * Based on the mission driven innovation policy framework: MinEZK (2019). Missies voor het topsectoren- en innovatiebeleid. Report structure

Two main sections: winning strategies and background analysis

Deep tech opportunities: Analysis: Answering the research questions to identify winning strategies Supporting evidence for the winning strategies

1. Scientific leadership Current Dutch position 2. Start-up A. Areas of opportunity activity

3. Economic potential Scope of B. Opportunities for impact intervention 4. Societal impact

7 Deep tech opportunities

8 Deep tech opportunities

A guide of our analysis of possible deep technology-industry combinations

• A. What are the most promising deep technology-industry areas for the future of the A.1 The stronger the deep tech Netherlands that can add value to the Dutch innovation ecosystem? ecosystem, the higher the score in 1. scientific • We analyse the transformative potential of deep technologies in different industries, based on the expectations of leadership and 2. start-up reports and roadmaps on the application of new technology. activity on a 1-5 scale based • For each deep technology we provide strategies to realise opportunities based on the current Dutch position. on the combined of indicators. • For each crucial industry, we relate the start-up ecosystem in that industry with the Economic potential. • A.2 Here each ecosystem is • We score each deep technology and crucial industry on its potential Societal impact. compared on a 1-5 scale based • Based on these results, we present three focus areas that represent high economic potential and/or societal on 3. economic potential (a impact and where the current Dutch position shows promise. combination of economic indicators), relating to active B. What are possible interventions in each technology-industry area so that public resources start-ups in the ecosystem. are used most effective? • A.3 Looking at the proposed • We formulate ‘reasons for intervention’, answering the question: what are signals to act on for specific deep solutions for missions we technologies? define the role of technologies • We analyse the existing start-up landscape to find hot spots (places where deep tech activity is abundant) and or industries as ‘driving’ or white spots (places where activity is expected but has not manifested). ‘supporting’ in a particular • We combine these analyses to explore the options public policy and public private partnerships have to intervene societal challenge for 4. in each of the focus areas. societal impact.

9 A. Areas of opportunity

Deep technologies have the potential to augment & transform industries

• SU ×ILLUSTRATIVEIndustry matrix? & NOT Chemical High tech Health & Financial Construction Energy Food Logistics ICT EXHAUSTIVE industry industries Pharma services

Navigation & Rehabilitation & BIM on site Assisted production (Serious) games Augmented & Virtual Reality hazard overlays accessibility

Process Predictive Smart Precision farming, Autonomous Autocorrected Data science, Diagnoses through Fraud detection, Artificial Intelligence & Data optimisation maintenance infrastructure food sorting supply chain processes automation data & imaging financial planning

Tracking materials Smart grid Validation of Multi-stakeholder Secure patient data Crypto, smart Ledgers Blockchain in supply chain transactions product origin supply chain transfer contracts

Equipment Production Crop monitoring & Supply chain (Security) devices, Sensing for Operations & safety Machine sensing Health monitoring Internet of Things optimization monitoring care tracking smart cities insurance

Production safety & Solar panel Harvesting & animal Autonomous Automation in Precision surgery, Automated building RPA Robotics maintenance production husbandry supply chain manufacturing social robots

Biobased, circular & Smart, selfhealing & Batteries & new Composites, (Selfassembling) Coatings Advanced materials novel compounds biobased materials catalysts ceramics & coatings biomaterials

Infrastructure Improved solar Semiconductor Sensing Bio-sensors Enhanced telecom Early diagnostics Photonics monitoring energy advances

Energy storage, Nanofluids, Nanomaterials Nanomaterials Nanoagrochemicals Nanotechnology membranes lab/organ-on-a-chip

Sensing, compound Secure Sensing, medicine Sensing Quantum Technology discovery communication development

Potential for augmentation: Potential for transformation: Level of expectations Impact to be determined Technology improves parts of value chain Technology changes entire value chain

Team analysis, based on expectations in: National Science Agenda & Knowledge & Innovation agenda Key Enabling Technologies | VNCI (2021). Actieagenda – Groene chemie, nieuwe economie | AINED (2018). AI voor Nederland – Vergroten, versnellen en verbinden | RVO (2019). Methodische bijlage: inzet op sleuteltechnologieën. | PwC (2018). Sizing the prize. | MGI (2018). Notes from the AI Frontier | Nationale Blockchain Coalitie (2017) Actieagenda. | MGI (2015). The Internet of Things: Mapping the value beyond the hype | Holland Robotics (2018). Kansen voor de 10 Nederlandse robotica – Samen investeren in toepassingsgerichte R&D | McKinsey & Company (2019). Industrial robotics - Insights into the sector’s future growth dynamics | PhotonDelta (2020). Photonics Roadmap 2020 | Quantum Delta NL (2019). Nationale Actieagenda Quantumtechnologie | BCG × HelloTomorrow (2017). From tech to deep tech. A. Areas of opportunity

The Dutch deep technology landscape is promising in multiple fields

Four possible strategies for deep technologies based on the Competitive Potential current Dutch position and economic potential: 5 Artificial Intelligence & • Potential winners: a high degree of scientific leadership arena Data winners and growing start-up activity provide the opportunity to acquire a worldwide top 10 position. Technologies in this field have a diversity of applications in different industries 4 with further room for exploration. Maintaining and Internet of Things Advanced materials expanding leadership and stimulating spin-off technology in NL in firms with scaling potential. 3 Robotics • Competitive arena: Scientific and start-up competition are fierce due to promise of economic potential and high

activity Nanotechnology Blockchain

Augmented & Virtual diversity of applications in almost all industries. Improving up

- Photonics Reality scientific leadership will help with finding and growing novel 2 applications.

Start Quantum technology • Early stage: Scientific leadership is promising, but Estimate of Diversity of applications are still at lower technology readiness. 1 Size = economic application Investing in moving from breakthrough to application will potential industries create economic potential in the long run. • Undecided: No overall strength for the Netherlands, but Undecided Early stage potential for specialised applications. Uncovering the 0 niches in which the Netherlands can lead. 0 1 2 3 4 5 Scientific leadership worldwide 5-point scores for start-up activity and scientific leadership are based on multiple variables. Scientific leadership is based on high quality publications per Million of inhabitants, the Dutch ranking in the world, the share of publications and academic spin-offs. Start-up activity is based on start-up population size, growth and investment size. For further details, see analysis at p. 11 19 and 23. Economic potential is a function of transformative potential (see p.9) and the economic strength of the affected industries, measured in jobs, exports, added value and R&D leadership, see analysis at page 29. A. Areas of opportunity

Not all industries see a comparable rate of deep tech start-up activity

Start-up activity for Deep technology in the Netherlands is 250 divided unequally, such that different ecosystems require different approaches. • High activity: deep tech start-ups have found these ICT

ups 200 High activity

industries as promising markets. The focus of these - ecosystems should be to find the right application areas and achieving scale ahead of the (international) Health & Pharma competition. 150 • Substantial activity: deep tech start-ups are trying to enter these industries with varying success. Focus in High tech industries these ecosystems should be on removing barriers that 100 Substantial activity prevent scaling. • Lagging activity: deep tech tart-ups are still underrepresented in these industries. Possible reasons Energy for this are the innovation dynamic or powerful 50

Number of deep tech starttech deep of Number Financial services Food incumbents in the industry. Focus should be on finding Lagging activity Construction niches for innovation in which deep technology can play a Logistics Chemical industry transformative role. 0 0 1 2 3 4 5 Economic potential of industry

5-point scores for economic potential are based on the economic strength of an industry, measured in jobs, exports, added value and R&D leaders. For further details, see analysis at p. 17. 12 Start-up data from Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. A. Areas of opportunity

Societal impact requires new combinations of technology and industry

Several deep technologies have an important role Energy & Food & water Health & well-being Safety & security to play in tackling societal challenges. These sustainability technologies are part of solutions that can be AR/VR implemented by crucial industries. The overview AI & Data represents expert assessment from the Dutch innovation policy framework on the role of Blockchain technology and industry. Internet of Things • Most deep technology has the promise to Robotics contribute to all societal challenges. AI & Advanced materials

Data, Internet of Things, Photonics, Photonics Nanotechnology and Quantum technology are Key Technologies Nanotechnology driving in one or more challenges. • Crucial for technology deployment across Quantum technology challenges are the High tech industries, Chemical Industry acting as supplier to other sectors such as Construction Energy, Food or Health & Pharma. Energy

Food

Logistics Considered a driving Considered a High tech industries technology or supporting ICT

industry for achieving technology or industries Crucial missions industry Health & Pharma Financial Services Based on expert expectations in: Integrale kennis- en innovatieagenda voor klimaat en energie Topsectoren (2019), Kennis- en Innovatieagenda Landbouw, Water, Voedsel (2021), Kennis- en innovatieagenda Veiligheid (2019), Gezondheid & Zorg: Kennis- en Innovatieagenda 2020-2023 (2019), and the underlying Perennial Mission driven Innovation programmes (MMIP). 13 Transformative capacity based on the descriptions of use of technology and role of industry. Additional data from TNO (2018). De potentiële bijdrage van technologie aan maatschappelijke uitdagingen and TNO (2017). Portfolioanalyse: kansrijke innovatieopgaven voor Nederland. A. Areas of opportunity

Deep tech is most promising in three technology-industry focus areas

Connecting technologies, industries and challenges leads to a clustering in which we identify three potential focus areas: Deep tech ● • HealthTech: centered around the Health & Pharma Industry ● industry with crossovers. • ClimateTech: centered around the Energy & sustainability Challenge ● challenge. • HighTech: focused on the most central industry for deep technology and societal challenges. See expanded rationale on the next page.

These are not the only possible clusters, but together these ClimateTech deep technology-industry combinations provide the greatest value to the Dutch innovation ecosystem: • They focus on long-term growth whilst addressing important societal challenges. HealthTech • The technologies that are key for each area provide potential for crossovers across multiple industries (such as nanotechnology) and societal challenges (such HighTech as AI & Data). • The contributing crucial industries represent important forces in the Dutch economy and deep tech innovation will contribute to their sustainable transformation.

14 Links between deep tech, industry and challenge are based on analysis on p. 9 and p. 12. A. Areas of opportunity

Deep tech is most promising in three technology-industry focus areas

HealthTech ClimateTech HighTech

Why Deep technologies can contribute to a wide variety of Deep technologies have a high transformative High tech industries are crucial for the position of the treatments in all stages of healthcare and contribute potential to contribute to energy & sustainability Netherlands in innovation and deep technology to well-being. There is a wide variety of start-up challenges across industries. Start-up activity is promises long term transformation. Dutch Scientific activity in this space, combined with Dutch scientific distributed across crucial industries with potential leadership is at the forefront. This is not translated to leadership in several key technologies, creating new start-up ecosystems, but is lagging overall and full-blown start-up activity, but the ecosystem several potential winners. requires scaling to create more impact. contains potential winners and early stage promises

Key • Artificial Intelligence & Data (competitive arena) • Advanced Materials (potential winner) • Robotics (competitive arena) technologies • Nanotechnology (potential winner) • Artificial Intelligence & Data (competitive arena) • Nanotechnology (potential winner) (not • Photonics (potential winner) • Photonics (potential winner) • Advanced materials (potential winner) exhaustive)

Crucial Driving industry to apply innovations is in Health & ClimateTech can be used across multiple industries to High tech industry acts as a driving supplier to almost industries Pharma, with supporting roles for ICT and abate emissions and create a circular economy: all societal challenges. At the same time it is also a Construction (for a healthy built environment). Health Energy, High tech industries, Chemical industry, main economic driver of Dutch exports (e.g. in & Pharma is an active start-up ecosystem, Construction, Food & Logistics. Activity in the latter semiconductors). The start-up ecosystem within the construction is still lagging. industries is still lagging. ClimateTech also connects industry has potential but is not fully formed. to multiple challenges in other areas, such as biodiversity, food security and critical infrastructure.

15 B. Opportunities for intervention

Deep tech winning strategies require a variety of approaches

• There is no single winning strategy. Depending on the deep technology and Industry ecosystem the state of the ecosystem, different interventions are appropriate (see right). Goal of winning • strategies However, interventions can take into account the unifying characteristics of Lagging activity Substantial activity High activity deep technology innovation: high uncertainty, long runways and large upfront investments. Interventions that derisk start-ups and support ecosystems in these areas will help ecosystem performance. Potential Find and remove Expand the start-up Invest in scaling & • Depending on specific bottlenecks for a particular deep tech the intervention winners industry barriers. field. long term growth. strategy can focus more on technology transfer, increasing technology market fit or helping ventures scale. Find new applications Find international Competitive Find niches where the and remove barriers opportunities for Public intervention is warranted when the current Dutch position provides signals Arena Netherlands excels. for further scaling specific technologies.

that indicate bottlenecks in the ecosystem. In our analysis we see the following positionup - signals: • Technologies that show high promise for industries, but with low start-up Find first scalable Monitor for new activity, may indicate difficulty in adoption by incumbents. Early stage applications for deep opportunities. • Technologies with high scientific leadership but low spin-off and start-up technology.

activity, may indicate bottleneck in technology transfer. start& Science • Technologies with high start-up activity but below average investments, may indicate difficulty finding product-market fit. Find niches where Undecided Observe. Netherlands excels. Using these signals and taking into account the hot spots and white spots of start- up activity we arrive at several possible interventions for each of the focus areas.

16 B. Opportunities for intervention

NL can strengthen hot spots and capture opportunities in white spots

Number of active deep Chemical Construc- High tech Health & Financial Hot spot White spot Energy Food Logistics ICT* technology start-ups Industry tion industries Pharma Services

Hot spots are areas where start-ups are already AR & VR 1 1 36 15 active in specific verticals. White spots are AI & Data 1 12 8 5 8 113 41 21 areas where start-up activity is expected but where little or no activity takes place. Blockchain 2 1 1 12 1 17 • Both HealthTech and HighTech are hot spots for start-up activity in most key Internet of Things 1 18 19 16 7 14 44 33 1 technologies, but feature some technological white spots (e.g. photonics in Robotics 1 7 8 5 44 2 22 Health & Pharma). • Currently, the ClimateTech grouping sees Advanced Materials 5 2 13 6 11 11 the most white spots: across crucial industries such as chemical, construction Photonics 5 16 1 4 and logistics, start-up activity is minimal, whereas the economic potential and Nanotechnology 2 4 2 19 43 societal impact is present. Quantum Technology 5 1 • Start-ups active in HighTech will also overlap with ClimateTech and HealthTech as more applications of deep tech become Climate High Health apparent. Tech Tech Tech

Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. 17 * High concentrations in of AI & Data and IoT in ICT are due to increasing prevalence of enterprise software start-ups in analytics for e.g. process optimisation, customer data analysis and asset management. These start-ups are generally not linked to specific industries but position themselves as generic b2b suppliers. B. Opportunities for intervention

Addendum: Biotechnology is key part of the HealthTech opportunity

Number of active deep Chemical Construc- High tech Health & Financial Number of deep tech firms that overlap with Energy Food Logistics ICT biotechnology technology start-ups Industry tion industries Pharma Services

AR & VR 15 Biotechnology is not a core part of our 1 analysis*, but plays an important role in the AI & Data 41 5 Dutch innovation ecosystem. It is strongly related to several areas of opportunity. Blockchain 1 • Biotechnology plays a key part in the HealthTech area, with a high concentration Internet of Things 33 3 of pure biotech companies in Health & Pharma and also overlapping with other Robotics 23 1 deep technologies, most notably nanotechnology. Advanced Materials 11 1 • There is lagging activity for biotechnology in other industries, which are currently Photonics 4 1 white spots but may provide more opportunities in the future. Nanotechnology 43 22

Quantum Technology

Biotechnology 3 1 6 1 71

Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. 18 * See Appendix for our characterisation of Biotechnology. B. Opportunities for intervention

Possible interventions for public sector support in the three focus areas

HealthTech ClimateTech HighTech Why intervention The health & wellbeing challenge has attracted Across industries deep tech start-ups have been The high tech industry in the Netherlands serves as may be significant deep tech investments in the Health & formed to address the energy & sustainability an innovation incubator for deep tech in multiple necessary Pharma industry. However, not all promising challenge. However, individually these pockets of industries. The ecosystem relies on research technologies have high start-up activity and funding, start-ups do not yet form a cohesive ecosystem and leadership in existing incumbents and there is signalling a potential missed opportunity. several technologies remain underfunded. relatively less start-up activity and funding HealthTech start-ups generally have longer runways ClimateTech also faces barriers in disrupting old (compared to e.g. ICT). Start-ups in this space often because of testing and regulation requirements. energy systems. remain a specialised supplier for other companies but do not grow beyond SME status.

Main questions • How can we strengthen the technological base in • How can we connect deep tech innovations • How do we grow the population of deep tech for successful start-ups? across industries? start-ups in high tech? intervention • How can we extend start-ups’ market reach? • How can we scale deep energy tech start-ups? • How can we create the next high tech unicorn in the Netherlands?

Options for • Identifying and helping promising start-ups in • Identifying and helping promising start-ups with • Stimulating start-up formation in intervention underfunded areas (advanced materials & multi-purpose technologies. underrepresented technologies (e.g. AI, advanced robotics). • Helping start-ups break systemic barriers to materials & photonics). • Internationalising the Dutch position in artificial create adoption for their technologies. • Helping existing start-ups transform from science intelligence & nanotechnology for Health. • Translating recent advances in advanced based tech start-up to fast growing industrial • Stimulating start-up formation for wellbeing materials, photonics & nanotechnology to start- organisation. technologies in other industries (such as ups with scalable products. • Activate existing industrial networks as launching Construction). customers for new technology.

19 Analysis: the current Dutch position in deep technologies

1. Scientific leadership 2. Start-up activity

20 Research framework

Mix of quantitative/qualitative indicators to analyse the Dutch position

Variable Indicators Metrics

• Scientific output • Selected publication output in top journals (Lens.org), ranked worldwide. • Scientific quality • Field weighted citation score (Elsevier) 1. Scientific leadership • Academic spin-offs • Science-based start-ups with publishing researcher as founder (Sciencefinder) • Thematic focus • Qualitative assessment of specific themes for NL (Desk research/Sciencefinder)

Current Dutch position

• Deep tech start-up population • Number of start-ups & growth of start-up population (Dealroom) 2. Start-up activity • (VC) investments • Total investments and average dealsize (Dealroom) • Technology readiness • Qualitative assessment of technology readiness level – TRL in NL

21 1. Scientific leadership: main overview

Technology 1. Scientific leadership

Estimated Output per Share of Academic Total rank in M of Assessment publications spin-offs world inhabitants Augmented & Internationally leading research position with high relative outputs. However, number of ■■■■ 4th 4,6 5,1% 2 Virtual Reality academic spin-offs is low.

Artificial AI & Data research has a significant amount of volume in both output and the number of ■ 24st 5,4 0,5% 35 Intelligence & Data academic spin-offs. The Netherlands does not have an internationally leading position.

Blockchain ■■ 17th 2,1 2,5% 0 Average research position but no academic spin-offs.

Lagging research position. However, application areas of research is wide and TRL-levels are high Internet of Things ■ 31st 0,8 0,2% 11 resulting (see 2. Start-up activity) in relatively large number of spin-offs. Robotics research is closely related tot AI & Data. Within this niche The Netherlands has an Robotics ■■■ 8th 5,1 1,4% 9 internationally strong position. Advanced Strong research ecosystem. Most academic research is relatively fundamental (low TRL, see 2. ■■■■ 8th 13,2 1,8% 12 materials Start-up activity). Relatively strong private R&D-research due to industry leaders. High research output. Large private R&D research due to presence industry leaders (see also 3. Photonics ■■■ 12th 14,4 1,2% 5 Economic potential) High research outputs and number of academic spin-offs. Field of research is connected to both Nanotechnology ■■■■ 15th 21,6 1,4% 11 photonics and quantum technology. Large private R&D and industry collaboration due to presence of industry leaders (see also 3. Economic potential). Quantum The Dutch quantum research ecosystem is among the leading ecosystems worldwide in both ■■■■■ 2nd 18,7 4,6% 6 technology quality and size. Research is mostly fundamental (see 2. Start-up activity).

Total score is summarised score of each metric, with highest score per metric as upper limit. All publication data from Lens.org. For selection, see p. 42. Estimated rank in world based on 22 number of publications per M of inhabitants on country level. Academic spin-off data from ScienceFinder, see p. 23. 1. Scientific leadership - Research output

The Netherlands ranks top 10 in 4 deep technologies for research output

The Netherlands is a high performing research nation that punches far Number of top publications per M inhabitants above its weight. With only 0.2% of the worlds population, it provides 0 5 10 15 20 25 2.1% of research output. Of the world’s 1% most cited articles, the 0 Netherlands has a share of 5.6%. For deep technologies: Quantum Augmented & virtual Technology • In terms of output per M inhabitants, Dutch researchers lead in 5 reality quantum technology, where they publish a significant volume of the Top 10 world’s research in top journals. This also holds true for advanced Robotics Advanced Materials position materials, robotics and augmented & virtual reality. 10 Photonics • In photonics and nanotechnology, the Netherlands does not hold a 15 top rank in terms of volume, but output quality is in the top 10 Blockchain Nanotechnology (FWCI). This is combined with industry collaboration rates that exceed the global average threefold. Strong players include Philips worldwideranking 20 HealthTech & ASML.* Artificial Intelligence 25

• Due to high competition in the fields of artificial intelligence and Estimated internet of things, the Netherlands lags behind in volume. Output % Dutch publications in total 30 Size = quality is still at ~1,5× the world average. In several subfields of AI Internet of Things volume of deep tech area and IoT the Netherlands is ahead in volume, such as Big Data and Sensor research.* 35

* Elsevier (2018). Quantitative Analysis of Dutch Research and Innovation in Key Technologies. Photonics and nanotechnology categories score high on the field weighted citation index 23 (FWCI), but average on global relative activity. AI and IoT score below average on activity but still high on FWCI. See appendix p. 43. Publication data: Lens.org. Publications from 2015-2020. Selection based on keywords and top journal selection. See appendix p. 42 for delineation. 1. Scientific leadership - Thematic focus

Two deep tech clusters that have unique Dutch research focus

• Deep technology advancement Overview of relatedness and focus of the Dutch scientific deep tech ecosystem (not comprehensive) does not exist in a vacuum. Advances in one technology field data mining, computer vision and can lead to breakthroughs and natural language processing for use applications in other fields. in i.e. medicine and energy. nanomaterials, -particles and -wires for Studying the relatedness in topics use in i.e. medicine, engineering between deep technology fields through keyword themes, we adaptive control and motion distinguish two clusters. planning (with AI use) for i.e. surgery • 1. A digital cluster containing AI, quantum computer, communications blockchain, AR/VR, robotics and IoT. distributed ledgers and smart contracts and software research • 2. A hardware cluster containing nanotech, quantum, photonics and waveguides, resonators and advanced materials. human-computer interaction and circuits for use in i.e. use of mixed reality for i.e. communications (mental) illness. • In each cluster and field of technology Dutch scientists study microstructures and coatings for specific subjects (see visual, not Powered by: design of smart grids and cities with i.e. use in i.e. construction and comprehensive). sensor networks and edge computing biomedical engineering

24 * Team analysis based on keyword selection by ScienceFinder. Technologies are related when more than 5 keywords overlap (out of 100 keywords/technology). 1. Scientific leadership - Academic spin-offs

Artificial Intelligence appears natural fit for academic spin-off activity

Known deep tech spin-offs 2010-2020 • Out of 892 academic spin-offs, we find 91 start-ups that are active in a deep Deep technology Areas of interest* 0 20 40 technology field.*

Augmented & Virtual Reality Haptics 2 • 38% of deep tech spin-offs are active in AI & Data. Artificial Intelligence & Data Analytics, computer vision 35 • The start-up propositions show strong 0 Blockchain - overlap with the research themes, suggesting that research results have Internet of Things Sensors, signals 11 been translated into Robotics Medical robots 11 entrepreneurship.

Advanced materials Concrete, ceramics, metal 12 • However, a leading scientific position in output and quality does not Photonics Specialised sensing 5 necessarily correlate with a larger share of spin-offs. Nanotechnology Lab-on-a-chip 9 • This discrepancy could depend on Quantum technology Enabling technology 6 technological maturity and entrepreneurship culture at different Powered by: universities.

*Team analysis, Based on data gathered by ScienceFinder based on the keywords gathered through lens.org. These are start-ups founded by at least 1 academic in a deep technology 25 research position. Not comprehensive. 2. Start-up activity: main overview

Technology 2. Start-up activity

Investment Start-up YoY Growth Total Population 2000-2020 in Technology Assesment 2015-2020 M€ readiness Augmented & ■■ 53 19% 15.6 8-9 High TRL but lacking number of start-ups and investments. Virtual Reality

Artificial Technology with highest investments and largest start-up population. Currently one of the most ■■■■■ 209 20% 608.7 7-9 Intelligence & Data used technologies in (deep) tech start-ups, with high TRL.

Fastest growing technology, but start-up activity remains low despite high TRL. Investments can Blockchain ■■■ 34 47% 187.6 7-9 predominantly be ascribed to one single start-up (see 3. Economic potential).

High TRL and widespread application of technology across industries, but average investment per Internet of Things ■■■■ 153 10% 118.4 7-9 start-up is lagging.

Low average investment per start-up compared to more software-based technologies in the Robotics ■■■ 89 8% 76.3 6-9 digital cluster.

Advanced High investments, but relatively small population of start-ups. Mostly lower TRL-level start-ups ■■■■ 48 8% 304.5 5-9 materials and possible market consolidation in relevant industries (see 3. Economic potential).

Photonics ■■ 26 12% 122.8 5-9 Small, slow growing population of start-ups but significant investments.

High total investments resulting from nanotech start-ups in Health & pharma. Despite relatively Nanotechnology ■■■ 70 6% 283.0 3-9 low TRL a significant start-up population.

Quantum Small start-up population due to low TRL. High degree of collaboration between start-ups and ■ 6 43% 4.2 3-7 technology academia (see 1. Scientific leadership).

Total score is summarised score of each metric, with highest score per metric as upper limit. Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees 26 and currently operational. 2. Start-up activity – numbers & growth

Start-ups in software-based technology more numerous and grow faster

• The population of start-ups in deep technologies that require large Total start-up population YoY Growth 2015-2020 (CAGR) hardware investments (Nanotechnology, Advanced Materials, Augmented & Virtual Reality 53 19% Robotics & Photonics) sees lower growth rates than in technologies that already Artificial Intelligence & Data 209 20% have established hardware (AR & VR, Robotics) or are more software based Blockchain 34 47% (AI). The population of hardware-based start-ups grew 8 % YoY in 2015-2020, Internet of Things 153 10% versus 15% for software-based start-ups. 2015 Robotics 89 8% • This also implies that start-ups in these 2020 hardware heavy fields face a longer Advanced Materials 48 8% runway, starting at lower technology readiness. Photonics 26 12% • Fastest growing field in absolute terms is Nanotechnology 70 6% Artificial Intelligence (125 new start-ups in 5 years), fastest growing field in relative Quantum Technology 6 43% terms is Blockchain (46% year over year).

27 Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. 2. Start-up activity – investments

AI, Nanotechnology & Advanced materials most funded ecosystems

• AI, Nanotechnology & Advanced materials gather Investment activity in start-ups Disclosed investments by deep tech in M€ most investments. Investments disclosed 120 Investments not disclosed • Despite significant start-up Size & 100 Artificial = Total investment activity, AR/VR, IoT and No known investors label Robotics start-ups attract Intelligence & Data 609

limited funding. Although Number of start-ups 80 investments hardware intensive, robotics 0% 50% 100% start-ups only manage to Augmented & Virtual Reality Augmented & Virtual Reality 7 10 36 60Internet of Things secure <5 M€ funding on 16 disclosed 118 average. Artificial Intelligence & Data 93 33 83 Quantum Nanotechnology with Technology Blockchain 7 6 21 40 283 4 • Highest average investment ups Internet of Things 57 28 68 - per start-up is in Advanced Robotics Advanced

Robotics Start materials, Blockchain and 24 24 41 20 76 Materials # # Photonics Advanced Materials 20 15 13 304 Photonics, due to a limited 123 Blockchain number of successful Photonics 14 5 7 188 companies in a small 0 Nanotechnology 43 9 18 - 5 10 15 20 25 30 35 population. Quantum Technology 2 2 2 Average investment per start-up (M€)

28 Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. 2. Start-up activity - Technology readiness

Deep technologies currently in research phase face long runway (5+ years)

Technology readiness range (indicative) • Hardware based deep technologies Research time to market have start-ups that start at lower levels 1 2 3 4 5 6 7 8 9 0 5 10 15 of technology readiness, in areas such as Advanced Materials, Robotics, Advanced materials - Research Nanotechnology, Photonics and Advanced materials - Start-ups Quantum Technology, creating a longer Artificial Intelligence & Data - Research Artificial Intelligence & Data - Start-ups runway (~6-12 years). Blockchain - Research Blockchain - Start-ups • Digital deep technology start-ups often Augmented & Virtual Reality - Research have at least a system prototype that is Augmented & Virtual Reality - Start-ups demonstrated in an operational Internet of Things - Research environment, making their product- Internet of Things - Start-ups market fit less risky and their runway Robotics - Research shorter (~5 years). Robotics - Start-ups Nanotechnology - Research Nanotechnology - Start-ups • Quantum technology is the only deep Photonics - Research technology where large scale Photonics - Start-ups deployment has not yet been Quantum technology - Research reached. Quantum technology - Start-ups

TRL 5 – Technology TRL 6 – Technology TRL 7 – System TRL 9 – Actual system TRL 8 – System TRL 1 – Basic TRL 2 – Technology TRL 3 – Experimental TRL 4 – Technology validated in demonstrated in prototype in proven in operational complete and principles observed concept formulated proof of concept validated in lab (industrially) relevant (industrially) relevant operational environment ready qualified environment environment environment for manufacturing

29 Team analysis, based on perspectives in the Knowledge and Innovation Covenant multi-annual research programs & start-up sample for each deep technology. Analysis: the scope of impact of deep technologies

3. Economic potential 4. Societal impact

30 Research framework

Mix of quantitative/qualitative indicators to analyse potential and impact

Variable Indicators Metrics

• Number of employees & share of exports (CBS/Eurostat) • Industry size • Employee growth, consolidation & fast growing firms (CBS) • Growth dynamic • Added value by industry (Eurostat) 3. Economic potential • Focus • Industry leadership in R&D (R&D top 1000) • Type of change in industries by • Qualitative assessment of industry position in NL (Desk research) and start-up new technology activity in specific vertical (Dealroom)

Scope of impact

• Potential contribution to challenges by technology and • Qualitative assessment of expectations potential use cases of deep technology 4. Societal impact required industry effort with new (Desk research). technology

31 3. Economic potential: main overview

Industry Economic potential

Number of Compound &

Share of Number of Things employees Share of Total value annual Total Dutch jobs Dutch R&D (x1.000) exports added (B€) growth in Reality in 2019 Leaders

2019 jobs ‘15-’19

Data Augmented Virtual Artificial Intelligence & Blockchain Internet of Robotics Advanced Materials Photonics Nanotechnology Quantum Technology

Chemical industry ■■■ 86.8 1.0% 7.6% 15.9 -0.5% 4

Construction ■■■ 402.4 4.6% 0.3% 20.0 0.3% 0

Energy ■■ 40.7 0.5% - 14.8 1.5% 2

Food ■■■■ 282.9 3.3% 8.6% 29.3 -1.0% 7

Logistics ■■■■ 374.8 4.3% 10.5% 27.4 2.1% 3

High tech industries ■■■■■ 332.8 3.8% 14.6% 34.9 1.8% 14

ICT ■■■■ 241.2 2.8% 1.1% 34.9 5.1% 6

Health & Pharma ■■■■ 623.9 7.2% 1.3% 32.3 1.9% 7

Financial services ■■■ 241.0 2.8% 0.6% 48.1 -2.1% 2

Total score is summarised score of each metric, with highest score per metric as upper limit. Employee and export data from Eurostat, added value data from CBS. Number of R&D leaders 32 based on the 2020 EU Industrial R&D Investment Scoreboard and the R&D Top 30 published by Technisch Weekblad. Colours per technology-industry combination based on analysis on p. 9. 3. Economic potential – Industry size

Significant differences in size and share of exports between industries

• Industries differ in both size and share of exports. Some industries have a ‘supporting’ role in the Dutch economy (low % of exports), Driving industries while others are ‘driving’ economic growth (high % of exports) and 15% thus differ in (potential) national and international impact. High tech industries Size = Total added value (€)

• Health & pharma and construction are the largest industries in terms of jobs. These industries mostly capture the general healthcare sector (excluding residential care and social work), and construction of Logistics 10% buildings and infrastructure. Almost all the economic activity in these industries is targeted towards the Dutch market and thus is supporting. Food Chemical industry

• Logistics, High tech industries, Food and the Chemical industry are Share Share exports* of (slightly) smaller industries in employees but together are responsible for ~42% of all Dutch exports. This corresponds to a total added value 5% Supporting industries of ~160 B€ per year. Hence these industries are of great importance for the earning capacity of the Dutch economy.

Health & Pharma • Financial services has the highest total added value per industry, but a Financial services IT Construction relatively low share of employment. 0% 0% 2% 4% 6% 8% Share of Jobs

33 *Exports of Energy industry not available. 3. Economic potential – Growth dynamics

Opportunities for deep tech in (large) industries needing renewal

• Industries have different innovation dynamics depending on 6% their growth, size and consolidation. Industries emerge and Emergence & Growth Sustainment grow from small size and a heterogenous population of firms 5% ICT and employees. Industries sustain themselves by Size = 2019 # employees consolidating, specialising and growing in size and decline 4% when disrupted by new entrants.*

• Deep tech can be a catalyst in industries that are required to 3% Construction High Tech industries transform themselves, to renew their growth. Food

19 in jobs in 19 Logistics

- 2% Health & Pharma Emergence High growth and new market ICT Chemical industry opportunities provide room for 1%

radical innovation CAGR 15 CAGR Growth Steady growth provides room for Construction, 0% adaptation with innovation Health & Decline Pharma, Food -1% Financial Services Sustainment Driven by competition and Logistics, High Energy consolidation, innovation has to Tech, Chemical -2% renew business industry -2 Less than -1average 0 1 More than2 average 3 4 Decline Driven by diminishing returns, Energy, Financial Consolidation new technology has potential to Services transform industry

* Menzel & Fornahl (2009). Cluster life cycles - dimensions and rationales of cluster evolution. 34 Data from CBS (2020). Consolidation is normalised for the share of total firms with more than 100 employees compared to the national distribution between small (<100 employees) and large firms (>100 employees) 3. Economic potential – R&D leadership

Opportunity: High Tech industry leadership can create new markets

Yearly R&D budgets (M€) of industry leaders* High Tech Industries • The presence of innovation leaders (i.e. global industry leading firms with high ASML 1,359 Janssen 413 R&D budgets) have positive influence on the (potential) impact an industry has. Philips 1,023 Uniqure 61 Food These innovation leaders develop in-house knowledge on a large scale and NXP 204 Synthon 60 VDL Groep 157 Merus 33 often bring deep-tech to market. In addition, they often have partnerships with Logistics Thales Nederland 127 Proqr Therapeutics 29 research insititutes and create spin-offs. KMW+Nexter 73 Sanquin 28 • The Netherlands several renowned industry leaders: ASML and Philips have Medical & Pharma TKH 61 Pharming 28 large market shares in their respected markets and both spend more than 1B€ Airbus defence and space 45 KPN 447 in R&D each year. Demcon 38 IT Gemalto 265 • In other industries Janssen (413M€) and KPN (447 €M) have high R&D budgets. 37 224 BE Semiconductor industries TomTom • Especially compared to their size High tech industries, Food, ICT and the 88 Chemical Industry SKF 24 Elastic Chemical industry have a large number of industry leaders in R&D, thus 30 Batenburg Techniek 22 NEDAP 15 increasing their economic potential for deep technologies. Financial services ASM 17 Technolution Royal ten Cate 15 Akzo Nobel 250 Rijk Zwaan 98 Energy DSM 205 FrieslandCampina 95 LyondellBasell 100 Enza Zaden 46 Tejin Aramid 16 Construction Corbion 40 Lely Industries 32 ING Groep 286 Marel Stork 32 Rabobank 118 COSUN 21 SBM offshore 20 DAF Trucks 165 Priva 18 Vanderlande Industries 60 Prodrive Technologies 52 None

35 Industry leaders and estimates of their annual R&D-bugets are based on the 2020 EU Industrial R&D Investment Scoreboard and the R&D Top 30 published by Technisch Weekblad. 3. Economic potential – start-ups in verticals

Concentration of deep tech start-up activity in ICT and Health & Pharma

Number of start-ups active in Chemical High tech Health & Financial Construction Energy Food Logistics ICT deep technology Industry industries Pharma Services

Augmented & Virtual Reality 1 1 36 15

Artificial Intelligence & Data 1 12 8 5 8 113 41 21

Blockchain 2 1 1 12 1 17

Internet of Things 1 18 19 16 7 14 44 33 1

Robotics 1 7 8 5 44 2 22

Advanced Materials 5 2 13 6 11 11

Photonics 5 16 1 4

Nanotechnology 2 4 2 19 43

Quantum Technology 5 1

Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. 36 * High concentrations in of AI & Data and IoT in ICT are due to increasing prevalence of enterprise software start-ups in analytics for e.g. process optimisation, customer data analysis and asset management. These start-ups are generally not linked to specific industries but position themselves as generic b2b suppliers. 3. Economic potential – start-ups in verticals

Investment highest in verticals ICT and Health & Pharma

Total disclosed investment 2000- Chemical High tech Health & Financial Construction Energy Food Logistics ICT 2020 per deep technology in M€ Industry industries Pharma Services

Augmented & Virtual Reality 4 11

Artificial Intelligence & Data 1 5 4 1 5 429 81 83 Messagebird: 236 M€ Blockchain 2 1 185 BitFury: 182 M€ Internet of Things 3 24 34 3 36 6 12

Robotics 2 27 40 7

Advanced Materials 120 11 140 34 Avantium: Mosa Meat: 119 M€ 83 M€ Photonics 47 75

Nanotechnology 15 2 31 235 UniQure: 53M€ Quantum Technology 4

Largest company in terms of total known invested capital in this technology × industry combination

37 Data: Dealroom, our classification. All companies founded after 2000, with at least 2 employees and currently operational. All amounts in rounded M€. 4. Societal impact – Deep technology expectations

Promised uses of deep tech to contribute to solutions (not exhaustive)

Deep Technology Energy & sustainability Food & water Health & wellbeing Safety & security Augmented & Training for emergency scenarios Virtual Reality Artificial Smart grid optimisation, predictive Crop sorting and selection, water use Patient data analysis and response New detection, protection and Intelligence & Data maintenance, autonomous driving optimisation, predictive harvesting (images, records, vital signs) response algorithms Blockchain Flexible energy contracts Automated certificates for food Traceable transfer of patient files Tamper-proof data storage and origins transfer Internet of Things Smart grids, sensors, maintenance, Sensors for data driven agriculture Smart wearables for Distributed infrastructure, automated demand response detection/prevention safety protocols Robotics Building retrofit automation, efficient Agriculture automation Precision surgery Automation or remote operation of production dangerous tasks Advanced Materials for solar energy production, Coatings for food preservation Biomaterials for medical applications (Self-repairing) new materials for materials Biobased and circular materials Defence for use in land, sea and air vehicles Photonics New PV cells, sensors for autonomous Sensing crops Imaging Laser satellite communication vehicles Nanotechnology Energy conversion, batteries & solar Sensing soil, filtration, Lab/organ on a chip, dna analysis, Data storage, forensics, devices for cells, carbon capture and green Nanoagrochemicals targeted drug delivery, imaging encryption electronics. Assessments are based on the considerations and Considered driving Indirect impact/not Mentioned for direct impact expectationsQuantum formulated in the Dutch Knowledge & Quantum sensing for diagnosticstechnology for attainingSecure communications, mentioned in support of mission goals InnovationBasedtechnology agendas on Integrale kennis- en innovatieagenda voor klimaat en energie Topsectoren (2019), Kennis- en Innovatieagenda Landbouw, Water, Voedsel (2021), Kennismission- en goalsinnovatieagendacryptography, authentication Veiligheid (2019), Gezondheid & Zorg: Kennis- en Innovatieagenda 2020-2023 (2019), and the underlying Perennial Mission driven Innovation programmes (MMIP). Transformative 38 capacity based on the descriptions of use of technology and role of industry. Additional data from TNO (2018). De potentiële bijdrage van technologie aan maatschappelijke uitdagingen and TNO (2017). Portfolioanalyse: kansrijke innovatieopgaven voor Nederland. 4. Societal impact – Industry expectations

Industries actively contributing to missions with deep tech (illustrative)

Industry Energy & Sustainability Food & Water Health & Wellbeing Safety & Security Electrification, decarbonised, biobased Removing particulate matter and Chemical Industry Protein conversion, reuse of materials and circular industry. microplastics, sensing Sustainable heating, renewables in the Construction Climate adaptive building Building healthy living environments built environment Sustainable energy production and Energy Biomass energy production infrastructure on sea and land Climate neutral food production, Reforming production processes and Food Healthy food products and advice carbon sequestration value chains Effective transport movements and Smart (predictive) defence and civilian Logistics Robust supply chains adopting zero emission technology logistics in complex situations Providing new (precision)farming New tools and machines for treatment, Maritime high-tech, satellite and space High Tech Industries Installing new energy systems equipment Installation of home devices technology E-health applications, health Data and algorithm innovation, ICT awareness offensive and defensive cybersecurity Improving accessibility to and quality Health & Pharma of healthcare interventions

Financial services Assessments are based on the considerations and Indirect impact/not Mentioned for direct impact Considered driving industry expectations formulated in the Dutch Knowledge & mentioned in support of mission goals for attaining mission goals InnovationBased agendas on expert considerations in: Integrale kennis- en innovatieagenda voor klimaat en energie Topsectoren (2019), Kennis- en Innovatieagenda Landbouw, Water, Voedsel (2021), Kennis- 39 en innovatieagenda Veiligheid (2019), Gezondheid & Zorg: Kennis- en Innovatieagenda 2020-2023 (2019), and the underlying Perennial Mission driven Innovation programmes (MMIP). Transformative capacity based on the descriptions of use of technology and role of industry. Appendices Deep technology scope

Technology clusters summarised and by source

Proposed summarised clusters: EU Advanced Technologies for Industry BCG × Hello Tomorrow NL Key Enabling Technology Clusters 1. Augmented & Virtual Reality Advanced Materials New Materials & Nanotech Advanced materials

2. Artificial Intelligence & Data Artificial Intelligence Artificial Intelligence & Data Digital technologies 3. Blockchain Big Data Artificial Intelligence & Data Digital technologies 4. Internet of Things 5. Robotics Cloud Computing Artificial Intelligence & Data Digital technologies 6. Advanced materials Blockchain Blockchain Digital technologies 7. Photonics Augmented and Virtual Reality Augmented/Virtual Reality Digital technologies 8. Nanotechnology Internet of Things IoT & Sensors Digital technologies

9. Quantum technology Connectivity IoT & Sensors Digital technologies 10. (Biotechnology) Advanced Manufacturing Technology Drones & Robots Engineering and fabrication technologies Regarding Biotechnology, see next page. Robotics Drones & Robots Engineering and fabrication technologies Nanotechnology New Materials & Nanotech Nanotechnologies

Micro- and Nanoelectronics New Materials & Nanotech Nanotechnologies

Photonics Photonics & electronics Photonics and light technologies

Quantum computing Quantum technologies

Industrial Biotechnology Life science technologies

Industrial Biotechnology Chemical technologies

Mobility

Security

BCG × HelloTomorrow (2017). From tech to deep tech. European Commission (2020). Advanced Technologies for Industry – General findings. RVO (2019). Methodische bijlage: inzet op 41 sleuteltechnologieën. Deep technology scope

Biotechnology is directly related to several deep technologies

• Biotechnology is wide in scope and encompasses several technological Branches of Biotechnology NL Key Enabling Technology Clusters Chosen Deep Tech Clusters trajectories. Environmental biotechnology Advanced materials Advanced materials

• The size of the biotechnology R&D ecosystem is larger than all other deep Process improving agriculture Advanced materials Advanced materials tech ecosystems and the innovation process (especially in medicine) is Bioinfomatics, computer science Digital technologies Artificial Intelligence & Data radically different than in other tech ecosystems. • The different branches and divisions of biotechnology are often referred Bioinfomatics, computer science Digital technologies Augmented & Virtual Reality to using colours. These colours represent the following categories. Bioinfomatics, computer science Digital technologies Internet of Things ▪ Green: for the development of agriculture. Bioinfomatics, computer science Engineering and fabrication technologies Robotics ▪ Yellow: for nutritional biotechnology. Industrial processes involving microoganisms Nanotechnologies Nanotechnology

▪ Red: devoted to medicine and human health. Medicine and human health Nanotechnologies Nanotechnology ▪ White: for industrial biotechnology. Environmental biotechnology Photonics and light technologies Photonics ▪ Gray: devoted to problems of environmental protection. ▪ Blue: for to the development of marine biotechnology. Medicine and human health Life science technologies Biotechnology ▪ Gold: which relates to bioinformatics and computer science. Marine Biotechnology Chemical technologies Biotechnology • Most of the seven divisions of biotechnology (partially) overlap with the Food and nutrition Biotechnology chosen deep tech clusters. Digital technologies Blockchain

Quantum technologies Quantum technology Therefore, we include biotechnology in this research only in the areas where it overlaps with other deep technologies (e.g. crossovers in AI and medical imaging, or nano medicine, or advanced materials).

42 RVO (2019). Methodische bijlage: inzet op sleuteltechnologieën. Dasilva, E.J. (2005). The Colors of Biotechnology: Science, Development and Humankind. Industry scope

Nine industries are studied to assess potential economic/societal impact

Industry Description Horizontal/ Driving/ • Industries are constructed from a subselection of NACE- Vertical Supporting industry classification in a way that they are Chemical recognizable and mutually exclusive. Manufacturing of chemicals and chemical products Vertical Driving industry • Industries are classified horizontal/vertical based on Construction of buildings, civil engineering and other specialised Construction Vertical Supporting activities their value chain integration. Industries that control a particular value stream are more vertical (energy, food), Mining, extraction of crude oils and gas, energy utilities and supporting Energy Vertical Supporting industries that provide services to a diverse set of value activities streams are more horizontal (finance, ICT). Crop and animal production, fishing and manufacturing of food Food Vertical Driving products • Industries are classified driving/supporting based on Transport activities, warehousing and related activities, and courier Logistics Horizontal Driving their role in economic growth. Industries that focus on activities. international competition and exports are driving (high High Tech Manufacturing of machinery, electrical equipment, motor vehicles, tech, chemicals) , industries that fulfil a home market Horizontal Driving industries metals, etc. need are supporting (construction, healthcare). ICT Telecommunications, computer programming and IT related services Horizontal Driving • These industries are considered to be crucial industries Health & Human health activities (excluding residential care and social work) and Vertical Supporting for the Dutch economy in terms of impact due to size, Pharma the manufacturing of pharmaceutical products. exports, growth dynamics and the presence of industry Financial Financial services such as banking, insurance and other auxiliary Horizontal Supporting leaders. Services activities

43 NACE-industry classification used as guidance in Eurostat and CBS. Challenge scope

Four challenge areas with goals based on Dutch innovation policy

Energy & Sustainability Food & Water Health & Wellbeing Safety & Security

• A fully CO2 neutral electricity • Circular food system • Reduction of illness as a • Reduction of organised crime system • Climate proof countryside and consequence of lifestyle • Innovation in the navy • A CO2 free built environment urban areas • Access to care is primarily • Operational space capacity for • Climate neutral and circular • Healthy and safe food organised around living defense and security resource flows • Sustainable and safe waters environment • Cybersecurity • Emissionless mobility • Protected Delta • Increased participation in society • Faster decision making through of people with chronic diseases • Climate neutral agricultural • Climate neutral agricultural digital technology and unmanned increases system system systems • Quality of life for people with • Faster innovation cycles in the dementia is improved department of Defence • Improved life expectancy in good • New data analytics health • Reduced health difference between socio-economic classes

44 MinEZK (2019). Missies voor het topsectoren- en innovatiebeleid. Scientific leadership

Keywords used for each deep technology delineation

Augmented & Artificial Quantum Blockchain Internet of Things Robotics Advanced materials Photonics Nanotechnology Virtual reality Intelligence & Data Technology Immersion (virtual Wireless sensor Open quantum Pattern recognition Cryptocurrency Control theory Composite material Photonic crystal Carbon nanotube reality) network system Natural language Mixed reality Smart contract Home automation Control engineering Metallurgy Waveguide Nanowire Quantum network processing Quantum Haptic technology Data mining Database transaction Smart city Mechatronics Microstructure Refractive index Nanomaterials information Computer-mediated Support vector Scanning electron Quantum error Distributed ledger Web of Things Control system Alloy Plasmon reality machine microscope correction Transmission Metaverse Feature extraction Ledger Smart grid Motion planning Polymer chemistry Optical fiber Quantum simulator electron microscopy Instructional Ultimate tensile Photonic integrated Deep learning Digital currency Edge computing Industrial robot Nanocomposite Quantum computer simulation strength circuit VRML Speech recognition Hash function Arduino Adaptive control Coating Resonator Nanorod Quantum sensor Convolutional neural Artificial reality Security token Raspberry pi Haptic technology Porosity Dielectric Self-assembly Quantum algorithm network Ubiquitous Finite element Genetic algorithm Consensus algorithm Teleoperation Nonlinear optics Monolayer Qubit computing method

These keywords were used to gather publication counts via Lens.org. The query was restricted to the 20 largest publications of high quality (median h-index > 5) and/or lists of top 45 conferences on the topics (specifically for Artificial intelligence & Data, Blockchain and Internet of Things). The name of the technology was included in the query (e.g. ‘nanotechnology’ was part of the keyword list). The keyword list was generated by taking the top 10 keywords for each technology field, controlling for overlap with other fields. Scientific leadership

Netherlands outperforms other countries in deep tech research quality

• Global Relative Activity Index (GRAI) compares Dutch activity to world output. On average, Dutch output keeps pace with the global growth of research Activity and Quality of Dutch deep tech research • Field Weighted Citation Index (FWCI) compares citations of Dutch research to world citations 1,8 SemiCondD and acts as a proxy for research quality (the more research is cited, the more important it is). NanoManuf PhoGenTech QuComp QuComm HFMSTech Label Description of subfield Deep tech field Label Description of subfield Deep tech field OEMMat StructMat 1,6 Blockchain PhoDet BioMat Bio (related) materials and soft material Advanced Materials Robotics Robotics Robotics IntPhoton CompCerms NanoMed BigData CompCerms Composite and ceramics Advanced Materials Bionano Bionanotechnology Nanotechnology NanoMat PV CyberPhySys SmartMat DesMetaMat Designer and meta materials Advanced Materials MicroFluidcs Micro and nanofluidics Nanotechnology DesMetaMat EnrgConv SensActua NanoDev AI Robotics EnrgConv Energy conversion Advanced Materials NanoManuf Nanomanufacturing Nanotechnology 1,4 BioMat Bionano EnrgStorg Energy storage materials Advanced Materials NanoMat Nanomaterials Nanotechnology ThinFilmCoats

OEMMat Optical/electronic/magnetic materials Advanced Materials NanoDev Nanoscale devices Nanotechnology

SmartMat Smart/self healing/self-organising Advanced Materials SemiCondD Semiconductor devices Nanotechnology EnrgStorg

StructMat Structural materials Advanced Materials NanoMed Nanomedicine Nanotechnology Field Weighted Citation Index 1,2

ThinFilmCoats Thin films and coatings Advanced Materials IntPhoton Integrated photonics Photonics MicroFluidcs AI Artificial intelligence Artificial Intelligence PhoGenTech Photon generation technologies Photonics

BigData Big data and data analytics Artificial Intelligence PhoDet Photonic detection Photonics FWCI average = 1 GRAI average = 1 1,0 Blockchain Block chain Blockchain PV Photovoltaics Photonics 0,4 0,6 0,8 1,0 1,2 SensActua Sensors and actuators Internet of Things QuComm Quantum communication Quantum Technology Global Relative Activity Index HFMSTech High frequency/mixed signal tech Internet of Things QuComp Quantum computing Quantum Technology

CyberPhySys Cyberphysical systems Internet of Things QuSensMet Quantum sensors and metrology Quantum Technology

Elsevier (2018). Quantitative Analysis of Dutch Research and Innovation in Key Technologies. Key technologies chosen by Elsevier based on Dutch Mission Driven Topsector and Innovation 46 Policy, categorised into deep tech fields through team analysis. Start-up activity

A selection of 687 start-ups active in deep technology

Sources: Cleaning and enrichment: • Dealroom: database provider on startups, growth companies, and tech • Based on the initial data exported from Dealroom, deep tech categorization ecosystem in Europe and around the globe. They are currently tracking ±1.8 was done manually by using top 5 unique keywords found in deep tech million startups & scaleups, ±100.000 investors and corporate. publications. • Pitchbook: database with financial information specializing on , • Requirement is that the start-up in this list is developing its own technology. private equity and merger & acquisition. They are currently tracking ±3 million Thus, we exclude the following to the list; companies, 330.000 investors and 59.000 funds. • Startups which only apply existing deep technology to their business model • E-commerce/consultancy selling deep technology (services). Filters: • Data was further cleaned manually to remove errors and industry data is further • HQ location: Netherlands categorized to follow the Dutch standard industrial classification. • Minimum of 2 employees • Funding data is further enriched by manual look up in Pitchbook for those • Not a subsidiary companies with “N/A” funding in Dealroom. • Founded in the last 20 years (>2000) • Is operational and not closed These steps result in a sample of 687 Dutch deep tech start-ups. • Contains deep technology keywords or tags, i.e. deep technology, artificial intelligence, machine learning, natural language processing, deep learning, 3D technology, augmented reality, big data, virtual reality, internet of things, blockchain, nanotech, autonomous & sensor tech, quantum technologies, laser technology, laser, optics, wave, industrial automation, simulation and computer vision.

47 All data acquired in December 2020 or January 2021. Colofon

Published: June 2021

Authors: Elmar Cloosterman, Dolfine Kosters, Bas van der Starre (Birch) & Bella Suwarso (Techleap).

Techleap supervision: Jaron Weishut, Koen Maaskant & Anne Strobos.

The views in this report are those of the authors and do not necessarily represent those of Techleap.

Contact Birch: [email protected]

Web: www.birch.nl