Int. J. Technology Management, Vol. 25, Nos. 1/2, 2003 181

The semiconductor community in the Silicon Valley: a network analysis of the SEMI genealogy chart (1947–1986)

Dimitris Assimakopoulos ESC-Grenoble, 12 Rue Pierre Semard, Grenoble 38003, France E-mail: [email protected]

Sean Everton and Kiyoteru Tsutsui Department of Sociology, Stanford University, Stanford, California 94305-2047, USA

Abstract: This paper focuses on the emergence of an informal, collaborative and entrepreneurial organisational culture within the technological community of semiconductor firms in the Silicon Valley, California. Using recently developed computerised network analysis techniques and a genealogy chart of Silicon Valley semiconductor firms, it demonstrates how a new democratic community, rather than a hierarchical workplace, organisational culture was firstly initiated with the foundation of Fairchild Semiconductor back in 1957, and more importantly, how this new culture was diffused through successive generations of ‘Fairchildren’ spin-offs, up to the mid-1980s. In the process of critical mass formation they also identified the most central companies in the semiconductor community, including the usual suspects, Fairchild, Intel and Hewlett-Packard, but also a relatively unknown firm outside the semiconductor community, Intersil Co.

Keywords: Silicon Valley; semiconductor industry; network analysis; Fairchild Semiconductor.

Reference to this paper should be made as follows: Assimakopoulos, D., Everton, S. and Tsutsui, K. (2003) ‘The semiconductor community in the Silicon Valley: a network analysis of the SEMI genealogy chart (1947–1986)’, Int. J. Technology Management, Vol. 25, Nos 1/2, pp.181-199.

Biographical notes: Dimitris Assimakopoulos is Associate Professor of Information Systems at Grenoble Graduate School of Business. His research focuses on social informatics, with particular emphasis on how new technological communities emerge across organisational and national boundaries and how informal and formal collaboration networks foster innovation in IT. Current research focuses on the role of venture capital and other private innovation support mechanisms in Information Society Technologies collaboration across the oceans.

Sean Everton is a PhD candidate in the Department of Sociology at Stanford University. His research interests include social network analysis, economic and political sociology, and the sociology of religion. Current research topics include the stratification of venture capital firms in the USA, the causes of denominational mortality in US Protestantism, and the political impact of evangelical renewal movements such as the Promise Keepers. He is also

Copyright © 2003 Inderscience Enterprises Ltd.

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involved in the preparation of a guide for visually representing social networks.

Kiyoteru Tsutsui is a PhD candidate in the Department of Sociology, Stanford University. His dissertation examines the evolution of the international human rights regime and its impact on ethnic social movements. His research centres around the impact of global norms on human rights, environmentalism and other progressive ideas on local social movements. His current research topics include the history of global human rights in the last five decades, the international human rights movement for former comfort women, and collective memories of war crimes in Japan. He is also interested in economic sociology and is developing a project on the evolution of business groups in Japan from the Meiji period to today. Tsutsui has been a MacArthur consortium fellow and remains an affiliate of the Center for International Security and Cooperation at Stanford.

1 Introduction

Many Silicon Valley (SV) firms have a SV semiconductor ‘genealogy chart’ hanging in their lobbies, that traces their roots back to Fairchild and Shockley [1]. The chart illustrates the entrepreneurial spirit that has spawned so many new start-ups in the area, and has made the Valley the centre of the high-tech industry in the world. Instead of staying in a large successful company, engineers and other employees of SV companies have traditionally preferred to launch their own start-ups, with the hope of striking it rich with the equity return. In moving to a new company, individuals brought social capital and know-how, which would make the companies in the SV operate on similar sets of ideas and principles. Thus, analysis of this chart provides insights into the organisational culture and network structure of the semiconductor community in the SV. We attempt such an analysis by using recently developed computerised network analysis methodologies. Social network scholar, Linton Freeman, has argued that the use of visual images is one of two factors that are primarily responsible for the development of social network analysis [2]. The other factor, he contends, is measurement. Network analysts continue to make remarkable progress in the measurement of social network data [3], and they have long used sociograms (i.e. network diagrams) to visually represent social structures [4]. However, while they have produced a number of computer programs that aid in the measurement of complex social structures [5], they have been much slower in developing comparable tools for visually representing these same social structures. A common technique that network analysts have used to draft a sociogram is to construct it around the circumference of a circle. The circle helps organise the data, but the order in which analysts place the points is determined only by their attempt to keep the number of lines connecting the various points to a minimum. Typically, researchers who use this technique engage in a trial-and-error drafting process until they reach an aesthetically pleasing result. While such a process can make the structure of network relations clearer, the relations between the sociogram’s points reflect no specific mathematical properties. The points are arranged arbitrarily and the distances between them are meaningless. Not surprisingly, how network data are spatially arranged in graphs influences how viewers perceive a network’s structural characteristics [6].

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Consequently, in recent years researchers have developed a number of techniques, such as metric and non-metric multidimensional scaling, which mathematically represent the nodes of a network in k-dimensional space. The goal is to portray the network data in a more ‘objective’ fashion. Moreover, several programs have been developed that make the visual analysis of social networks more readily available to network analysts. Promising among these are the molecular modelling programs originally developed for chemists and biologists that also provide elegant images of social networks [7]. These images provide visual representations of social networks that highlight the set of actors and the ties between them. They uncover the structure of social ties but typically require a substantial amount of interpretation and can raise more questions than they answer. This has led most social network analysts to use such images primarily as a starting point for a more systematic analysis of how structures arise and change over time, and as preludes to more complex quantitative analyses [8]. Some network analysts, however, have begun to draw upon visual representations of social networks in order to uncover details of network structures that are not readily apparent [9]. In this paper we use one of these programs, Mage [10], which was developed as a device for molecular modelling, to produce three-dimensional illustrations of the SEMI ‘genealogy chart’. Our main aim is to visually represent and uncover previously unseen details of the origins and emergence of the community of semiconductor firms in the SV. The paper is subsequently divided into five sections. Section 1 sketches a brief history of the origins and emergence of the SV’s semiconductor community. One of SV’s ‘urban legends’ is the story of how William Shockley’s transistor laboratory (Shockley Transistors) ‘gave birth’ to Fairchild Semiconductor as eight of his top employees left out of frustration with his management style. Fairchild, in turn, later gave birth to other firms such as Advanced Micro Devices, Four Phase, Intel and LSI Logic, many of which, then, gave birth to other important firms of the SV. After reviewing this history, Section 2 introduces the notion of critical mass in the diffusion of innovations. Then Section 3 discusses some key issues related to network data and methods with regard to the analysis of the SEMI ‘genealogy chart’. Section 4 presents the results of our analysis using visual representation techniques to uncover the evolution of the SV’s semiconductor community and identify its central company/actors in the SEMI ‘genealogy chart’. Combined with the results of network analyses, these visual images give us some hints as to why the informal, democratic, collaborative and entrepreneurial culture that was born with the foundation of Fairchild has become the prevalent way of doing things in the SV. We conclude with an evaluation and summary of key findings, a commentary on the value of underlying concepts and methods, and suggestions for future research.

2 The origins of the SV’s semiconductor community

On 23 December 1947, William J. Shockley, John Bardeen, and Walter Brattain of Bell Laboratories in Murray Hill, New Jersey, demonstrated the first successful transistor. These three inventors eventually won a Nobel Prize for this achievement, but only Shockley was determined to capitalise on it. He left Bell Laboratories in 1954 for New England to become a consultant for Raytheon Corporation with the hope of

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establishing a semiconductor firm. However, after Raytheon refused to guarantee him $1 million in seed money over three years, he left after only one month, and headed back to his native Palo Alto. There, with the backing of Arnold Beckman, the president of Beckman Instruments, and the encouragement of Frederick Terman, the engineering dean at Stanford, he started his own company, Shockley Transistors, in 1955 [11]. By most accounts Shockley was a genius for spotting and recruiting talent, but he was not as adept at managing it. Within two years of its founding, Shockley Transistors was experiencing severe internal turmoil, largely due to Shockley’s bizarre management style [12] and his decision to concentrate on four-layer diodes rather than the product implied by the company’s name [13]. All of this ultimately led a group of his employees to approach Beckman with a plan that would grant Shockley emeritus status in the company but remove him from the day-to-day operations of the firm. Shockley vetoed the plan, however. As a result eight of his employees left in 1957 – Shockley branded these defectors the ‘traitorous eight’ – in order to found Fairchild Semiconductor. Shockley Transistors never recovered from the loss. It did, however, hang on until 1968 when Beckman sold it to Clevite, which in turn sold it to ITT, which eventually shut it down. Fairchild, on the other hand, met with almost immediate success. It initially manufactured high-frequency transistors based on techniques developed at Shockley. It was turning a profit by the end of 1958. The timing of its founding could not have been better. By 1957 there was sufficient demand from manufacturers who merely wanted transistors instead of vacuum tubes, for use in radios and other machines, to justify the new operation. But it was also in 1957 that the Soviet Union launched Sputnik I. In the electronics community the ensuing space race had the effect of coupling two new inventions – the transistor and the computer – and magnifying the importance of both [12, p.358]. Robert Noyce, one of the traitorous eight, helped develop the first integrated circuit, which eventually turned Fairchild into one of the SV’s most profitable companies. Strictly speaking, Noyce was not the first to develop an integrated circuit. That honour belonged to Jack Kilby of Texas Instruments [14]. But six months after the announcement of Kilby’s invention, Noyce created a similar circuit except that it was made of silicon and used a unique insulating technique developed by Jean Hoerni, another one of the traitorous eight. Noyce’s device turned out to be more efficient and more practical to make than Kilby’s and eventually became the industry standard [12, p.358]. More importantly, it helped turn Fairchild into the semiconductor community’s leader and put the SV on a path that would eventually lead to its dominance in the high technology community. In spite of the impact that integrated circuits have had in the SV’s development as a technological leader, Fairchild’s importance for the SV transcends this particular technological breakthrough. Under Noyce’s leadership Fairchild also contributed a vision of management that explicitly rejected the hierarchical East Coast corporate culture [15]. This was perhaps, in part, in reaction to Shockley’s authoritarian management style, but regardless of its roots, this new approach diffused as employees from Fairchild left to start their own companies. Researchers and journalists often highlight two components of the unique organisational culture found in SV companies: a community-like atmosphere within the companies and entrepreneurial spirit that spawns many start-ups. Firstly, the management style of SV companies is in stark contrast to that of East Coast firms.

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Corporations in the East adopted a feudal approach to organisation, without even being aware of it. There were kings and lords, and there were vassals, soldiers, yeomen and serfs, with layers of protocol and perquisites, such as the car and driver, to symbolise superiority and establish the boundary lines. Fairchild Semiconductor needed a strict operating structure, particularly in this period of rapid growth, but it did not need a social structure. Noyce rejected the idea of a social hierarchy at Fairchild. Everywhere the Fairchild émigrés went, they took the Noyce approach with them. It wasn’t enough to start up a company; you had to start a community, a community in which there were no social distinctions, and it was first come, first served in the parking lot, and everyone was supposed to internalise the common goals. The atmosphere of the new companies was so democratic, it startled businessman from the East [12, p.360]. Fairchild also contributed to the development of a second key organisational culture of the Valley that encourages entrepreneurial risk-taking: a culture that Saxenian argues has played an important role in the SV’s success [15]. According to Saxenian, in places where failure is frowned upon, it is less likely that people will engage in high-risk activities such as business start-ups. She points to the risk-averse culture of the high technology industrial region of Route 128 in Massachusetts that gives births to few start- ups as an example. The SV’s culture, on the other hand, supports risk-taking and this has led to a large number of start-ups, and such entrepreneurial activity can be traced back to the traitorous eight who left Shockley Transistors to form Fairchild Semiconductor. If such entrepreneurial risk-taking had stopped there, then perhaps the start-up culture would not have taken a hold in the SV, but it did not. When Fairchild was only one-and- a-half years old, its first general manager, Ed Baldwin, left in order to form Rheem Semiconductor, taking ten key Fairchild employees with him [16]. And when relations between Fairchild and its parent firm, Fairchild Camera and Instrument Corporation, became strained, four of its original founders pulled out in 1961 in order to form Amelco (now Teledyne Semiconductor). In the same year several key Fairchild employees left to found Signetics. Undoubtedly, however, the most important spin-off occurred in 1968 when Robert Noyce, along with Gordon Moore and Andy Grove, left to start Intel. By 1986 31 semiconductor firms could directly trace their ancestry to Fairchild. And, of course, these do not include those that can indirectly trace their roots to Fairchild whose founders came from firms such as Intel that were founded by Fairchild employees.

3 The concept of critical mass in the diffusion of innovations

The diffusion of innovations, both technical and organisational, usually takes many years. Past research has shown that the diffusion process of an innovation follows a normal, bell-shaped curve when plotted over time on a frequency basis. This bell-shaped curve was first divided by Rogers into five adopter categories (see Figure 1) using the standard deviation from the average time of adoption: innovators, early adopters, early majority, late majority, and laggards [17].

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Figure 1 Innovativeness and adopter categories

Source: Rogers [17, p.247] When the cumulative number of adopters is plotted, the result is an S-shaped curve (see Figure 2). It is generally accepted by diffusion scholars that it is a relatively easy task to plot such a curve for a particular innovation. What is far more difficult is to explain how and why diffusion happens. There is a ‘take off’ point on this curve when the rate of adoption is around 20%, when a critical mass of adopters has been achieved, and the diffusion process is too late to be reversed. The notion of critical mass is most often used as a metaphor in the diffusion of innovations literature. This is because it is difficult to quantify when the 20% of adopters has been reached. Note that most diffusion studies take place while the adoption is occurring and the total number of adopters is not known. Valente [18] defines the critical mass as early adoption by key actors in the social networks underlying the diffusion process.

Figure 2 Cumulative number of adopters of an innovation by time and critical mass

Source: Rogers [17, p.246]

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When scholars talk about diffusion of innovation, they generally talk about it in terms of technological advances. For example, in the economic literature, Freeman and Soete point out how significant improvements to an innovation occur as diffusion unfolds [19]. In this paper, however, we argue that Fairchild’s informal and collaborative culture was an innovation, in and of itself, and over time it diffused throughout the SV semiconductor community and beyond [20]. As we noted at the outset, to illustrate this process, we draw on methods developed for analysing and visualising social networks, and it is to an explication of the relevant methods that we now turn.

4 Network data and methods

Network data and methods focus on relationships among actors such as individuals and companies, rather than on the intrinsic attributes of the actors themselves. The SEMI ‘genealogy chart’ provides relational information on founders of companies in the SV’s semiconductor community and the founders’ previous company affiliation. All the companies are represented in boxes in chronological order of the founding date from left to right, from 1947 to 1986. Two companies are connected in the chart when one is founded by someone from the other. While the chart portrays the community’s evolution over time, the mere connections between companies are very difficult to identify because of the volume and complexity of ties. More importantly, it is difficult to comprehend the processes of spin-off creation and building up of critical mass. Both processes are essential in understanding the process of diffusion of informal and collaborative culture, first initiated in Fairchild, across the region. In order to gain an increased understanding of the emergence of critical mass and in particular the relationships between parent and spin-off companies, a different visualisation methodology is necessary. The use of a social network analysis program such as Ucinet 5, in conjunction with visualisation software such as Mage, fulfils both objectives. Additionally, it enables an increased understanding in terms of identifying the community’s central companies. However, a limitation that arises from using Ucinet and Mage, is that both these programs cannot handle the time dimension that the original chart has. To address this limitation, we divided the years the genealogy chart covers into six periods (1960, 1965, 1970, 1975, 1980, 1986) and traced the change in the network’s structure and its central actors according to these six points in time. We initially created six (one for each year in our analysis) previous-by-founded company matrices, where the rows represent founders’ previous company affiliation and columns represent the companies they founded, such that xij equals the number of founders ‘sent’ from company i to found company j. Thus, the value of the cell equals eight where the previous (sending) firm is Shockley Transistors and the founded (receiving) firm is Fairchild Semiconductor. We followed the most common method of visualising two-mode networks by converting the six two- mode matrices into one-mode previous-by-previous company matrices, which are calculated by multiplying the original two-mode matrices by their transposes. In the resulting one-mode matrices, a tie exists between two companies if individuals from the two companies joined to form a third company. In other words, when somebody from company ‘A’ joined forces with somebody from company ‘B’ to found company ‘C’, a tie exists between companies ‘A’ and ‘B’. Thus, in 1960 a tie exists between Fairchild

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Semiconductor and General Atomic because individuals from both firms joined forces to give birth to Rheem Semiconductor. We assume that such ties indicate both formal and informal connections between the various founding firms. That is, the individuals who came together to give birth to new companies did not do so as strangers, but rather as people who had previously met and formed working relationships with one another. Our argument is that over time, as the number of ties between founding firms grew and resulted in an increasingly denser network, Fairchild’s collaborative culture spread throughout the SV, as it built up the necessary critical mass of working relationships for diffusing the horizontal, community- like organisational culture, first initiated in Fairchild. The growing number of founding of firms, in and of itself, is also evidence that the entrepreneurial spirit became increasingly prevalent and took root in SV. The next step in the visualisation process is creating the coordinates necessary for plotting the sociograms in three-dimensional ‘space’. To do this we used non-metric multidimensional scaling beginning with a random starting configuration [5]. The stress levels for all six matrices were less than .02, which indicates an excellent goodness of fit [21]. Then we combined the six sets of coordinates with the original sociomatrices to produce the kinetic image files that can be read by the Mage program [7]. We also computed actor degree and betweenness centrality measures at the six points in time. Degree centrality is simply the number of other actors to whom a given actor is connected. This measure of centrality is typically used to measure an actor’s involvement in a network [3,22]. In the ‘genealogy chart’, one could argue that a founding company tied to two other founding companies could be said to be twice as involved in the founding of other firms as a firm with only one. Betweenness centrality, on the other hand, is generally interpreted to be a measure of an actor’s power [2,7,22]. The assumption is that an actor has power over any two other actors when it lies (on the shortest path) between the two in a given network of relations. Thus, in a given network of N actors, an actor obtains a maximum of one when all other N-1 actors are tied only to that actor, and it obtains a minimum of zero when it lies on no shortest paths between any two other actors in the network.

5 Results

In this section we present the visual representations of the previous company by previous company sociomatrices for each of the six points in time. The ranking of semiconductor companies on their centrality measures accompany the image for each period, clarifying who the central and powerful actors in the community were in each period. It is important to note at the outset that these images for different periods are independent of each other and the location of each node changes with each image. In the first period in our analysis (see Figure 3), the community is at its early stage and no clear central actor has emerged. The community network consists of nine actors and is rather symmetric with all but two of the nine companies being connected with others. The two unconnected firms are Shockley Transistors and Sperry Semiconductor. Their isolation in the graph indicates that while employees from these two firms were involved in the founding of new firms, these employees did not co-found these new companies with employees from other firms. Hence, the image shows no tie between them and the other seven firms in the graph. It is fascinating, however, that even at this

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early stage in the community, employees from seven firms joined together in the founding of a new semiconductor company (Rheem). While hindsight is almost always 20-20, this seems that this may be an early indication of the collaborative culture that would later emerge and come to define SV’s semiconductor community in particular and its high technology community in general.

Figure 3 Non-metric multidimensional scaling of SV’s semiconductor community, 1947–1960

Turning to our centrality measures (Table 1) we can see that of the network’s nine actors, seven share the same measures of degree centrality. This reflects the fact that all seven are connected similarly with one another and no single actor stands out as the central actor. This fact is perhaps better captured by the betweenness centrality measures, all of which equal zero, indicating that no single actor, at this point in time, is more ‘powerful’ or influential than the others.

Table 1 Centrality ranking of founders of SV’s semiconductor companies, 1947–1960 [N=9]*

Degree Centrality Betweenness Centrality 1. Bell Telephone Laboratory 75.00 0.00 1. Fairchild Semiconductor 75.00 0.00 1. General Atomic 75.00 0.00 1. General Transistor 75.00 0.00 1. Hughes 75.00 0.00 1. Morris-Knudsen 75.00 0.00 1. Standard Oil 75.00 0.00 8. Shockley Transistor 0.00 0.00 8. Sperry Semiconductor 0.00 0.00 * The ranking is based on the degree centrality score for all periods

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By 1965 the network had grown to fourteen actors, but as Figure 4 illustrates, in 1965 the SV’s semiconductor community was still in an embryonic stage and appears to have changed very little since 1960. The seven companies that were connected with each other in 1960 remain the community’s central sector, but an eighth company has joined the group: namely, the United States Marine Corps (USMC). However, USMC is only connected to one of these firms, namely Fairchild. What in fact occurred was that in 1963 three employees from Fairchild and one from the USMC joined together to found General Micro Systems. Because of this co-founding, by 1965 Fairchild began to show signs of becoming the semiconductor community’s central actor, at least in terms of the founding of new semiconductor companies. Figure 4 also indicates the presence of a tie between two companies apart from the community’s central sector. This tie reflects the co-founding of Siliconix by employees from Texas Instruments and Westinghouse.

Figure 4 Non-metric multidimensional scaling of the SV’s semiconductor community, 1947–1965

Table 2 underscores this point further. In 1965 Fairchild had a slightly higher normalised degree centrality (53.85) than the rest of the other actors in the network had. It was also the only actor to register a betweenness centrality score (7.69), which reflects the fact that it lay on the shortest path ‘between’ USMC and the six other actors in the network connected with one another. As we discussed earlier, researchers typically interpret betweenness centrality as a measure of an actor’s power. Such an interpretation is probably inappropriate in this context, however. It is perhaps better seen here as a measure of the heterogeneity and extent of a firm’s ties. In 1965 Fairchild was not only connected to the six firms identified earlier but also to the USMC, which had no ties to the other six. Thus, rather than solely reflecting the number of ties a firm has with other firms, the betweenness measure takes into account the heterogeneity of a firm’s ties.

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Clearly, the number of ties a firm has with others influences a firm’s betweenness score, but as is shown later, some firms’ betweenness centrality scores are actually higher than their degree centrality scores. Table 2 also indicates that Shockley and Sperry have dropped off the top-ten centrality list, being replaced by Texas Instruments, USMC and Westinghouse.

Table 2 Centrality ranking of founders of SV’s semiconductor companies, 1947–1965 [N=14]

Degree Centrality Betweenness Centrality 1. Fairchild Semiconductor 53.85 7.69 2. Bell Telephone Laboratory 46.15 0.00 2. General Atomic 46.15 0.00 2. General Transistor 46.15 0.00 2. Hughes 46.15 0.00 2. Morris- Knudsen 46.15 0.00 2. Standard Oil 46.15 0.00 8. Texas Instruments 7.69 0.00 8. USMC 7.69 0.00 8. Westinghouse 7.69 0.00

By 1970 the SV’s semiconductor community had grown to 35 companies, a sharp increase from the 14 present in 1965. As Figure 5 indicates, the network is growing both in size and complexity. As the number of actors in the network increases, the structure that will shape the SV’s semiconductor community in later periods begins to form. Other semiconductor companies were clearly active in the founding of new firms, but still lying at the centre is Fairchild along with the six other firms it joined to found Rheem.

Figure 5 Non-metric multidimensional scaling of the SV’s semiconductor community, 1947–1970

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As Table 3 indicates, Fairchild’s normalised degree centrality is more than twice that of any other actor in the network while its normalised betweenness centrality is almost three times that of the only other actor in the network that at this point in time registered a measure of betweenness centrality (Hewlett-Packard).

Table 3 Centrality ranking of founders of SV’s semiconductor companies, 1947–1970 [N=35]

Degree Centrality Betweenness Centrality 1. Fairchild Semiconductor 35.29 12.30 2. Bell Telephone Laboratory 17.65 0.00 2. General Atomic 17.65 0.00 2. General Transistor 17.65 0.00 2. Hughes 17.65 0.00 2. Morris- Knudsen 17.65 0.00 2. Standard Oil 17.65 0.00 8. Hewlett-Packard 11.77 4.28 9. General Instrument 5.88 0.00 9. IBM 5.88 0.00 9. Intersil 5.88 0.00 9. Mellonics 5.88 0.00 9. Signetics 5.88 0.00

Hewlett-Packard’s emergence in 1970 as a central actor in the community is worth highlighting. By 1970 former Hewlett-Packard employees had been involved in the founding of two new semiconductor firms: Qualidyne and Signetics Memory Systems. One former Hewlett-Packard employee joined with two from Fairchild and one from Intersil to found Qualidyne, while another joined with two from IBM and two from Signetics (which was founded by six employees by Fairchild) to found Signetics Memory Systems. And as Saxenian has noted [15], Hewlett-Packard has played a central role in the emergence of SV’s unique culture as well. She argues that in SV there exists a tradition of informal exchange of information at social gatherings, trade association meetings and community conferences. People who gather at these settings discuss the latest technological subjects and engineering concerns, and she traces this tradition of informal exchange, in part, to David Packard’s and William Hewlett’s willingness to extend their personal assistance to other firms and entrepreneurs in the area, even to those who were their direct competitors. As Figure 6 illustrates, by 1975, the network structure of the community had begun to take a shape that resembles the most recent period in our analysis. Nineteen new semiconductor firms were founded in SV during the five-year period from 1970 to 1975, bringing the community’s total to fifty-four. What is clear from the image, however, is that lying at the centre of this network were a handful of firms that seem to have been more active in the founding of new firms than others in the network. Fairchild once again lies at the centre of the image, and its centrality is verified by its high degree and betweenness centrality scores (see Table 4), but other actors, such as Intersil and Hewlett-Packard, are beginning to emerge as influential players as well. Both lie close to Fairchild in the image.

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Figure 6 Non-metric multidimensional scaling of the SV’s semiconductor community, 1947–1975

As Table 4 indicates in terms of their degree centrality Hewlett-Packard and Intersil ranked behind only Fairchild, Bell Labs and Hughes, while in terms of their betweenness centrality Hewlett-Packard ranked behind only Fairchild while Intersil ranked behind only Fairchild, Hewlett-Packard and American Microsystems.

Table 4 Centrality ranking of founders of SV’s semiconductor companies, 1947–1975 [N=54]

Degree Centrality Betweenness Centrality 1. Fairchild Semiconductor 32.08 16.92 2. BellTelephone Laboratory 18.88 1.39 2. Hughes 18.88 1.39 4. Hewlett-Packard 13.21 8.00 4. Intersil 13.21 2.65 6. American Microsystems 11.32 2.99 6. General Atomic 11.32 0.00 6. General Transistor 11.32 0.00 6. Morris- Knudsen 11.32 0.00 6. Standard Oil 11.32 0.00

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By 1980, the SV’s semiconductor community was beginning to take shape (see Figure 7). By then there were 63 actors in the network, and while Fairchild still lay at its centre, the central sector appears to have grown and become increasingly interconnected. Furthermore, there were fewer isolates in 1980 than in 1975. While in 1975 19 out of the 54 actors (35.19%) in the network were isolates, in 1980 only 17 out of the 63 actors were (26.98%).

Figure 7 Non-metric multidimensional scaling of the SV’s semiconductor community, 1947–1980

In 1980 Fairchild continued to be the most central actor in the community by both measures of centrality, while Hewlett-Packard, Intersil and American Microsystems retained the high levels of centrality they began to demonstrate in 1975 (see Table 5). In fact, the betweenness measures of centrality indicate that Fairchild, Hewlett-Packard, Intersil and American Microsystems were the most central actors in the community.

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Table 5 Centrality ranking of founders of the SV’s semiconductor companies, 1947–1980 [N=63]

Degree Centrality Betweenness Centrality 1. Fairchild Semiconductor 35.48 25.84 2. Bell Telephone Laboratory 16.13 1.41 2. Hughes 16.13 1.41 4. Intersil 14.52 7.25 5. Hewlett-Packard 12.90 13.21 6. American Microsystems 11.29 5.17 7. General Atomic 9.68 0.00 7. General Transistor 9.68 0.00 7. Morris- Knudsen 9.68 0.00 7. Standard Oil 9.68 0.00

Our final image (Figure 8) captures the state of the community in 1986. At that time the community consisted of 102 firms and was even more interconnected than it was in 1980. Of the 102 founding companies only 23 were isolates (22.55%).

Figure 8 Non-metric multidimensional scaling of the SV’s semiconductor community, 1947–1986

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The central actors largely remain the same, as Fairchild, Hewlett-Packard, and Intersil occupy the top of the centrality ranking (see Table 6). One remarkable development is the emergence of Intel as the second and third most central actor in degree and betweenness centrality respectively. It was not an influential actor at all in as recent as 1975, but because of the influence of its founders, who are among the ‘traitorous eight’, it has become the most important firm among the ‘Fairchildren’ by 1986. We suspect that Intel’s centrality has grown even further in the years following this period, but this notion can only be confirmed by analysis of more recent data on the community.

Table 6 Centrality ranking of founders of SV’s semiconductor companies, 1947–1986 [N=102]

Degree Centrality Betweenness Centrality 1. Fairchild Semiconductor 28.71 19.08 2. Intel 16.83 8.98 3. Hewlett-Packard 11.88 9.16 3. Intersil 11.88 7.84 5. Bell Telephone Laboratory 9.90 0.63 5. Hughes 9.90 0.63 7. National Semiconductor 8.91 4.90 8. American Microsystems 7.92 1.45 8. Signetics 7.92 3.02 10. Motorola 6.93 2.09

6 Evaluation and conclusions

The series of graphs and tables above illustrate in some depth the evolution of the SV’s semiconductor community. Starting with nine largely indistinguishable firms in 1960, the community has evolved into a complex network of ties among more than 100 companies in 1986. In the process, Fairchild emerged as the most central actor in the community as early as the late 1960s, and maintained its status throughout the stages of its development. ‘Fairchildren’ companies such as Intel and Signetics also score high on centrality measures in subsequent stages. These findings underscore the central role of Fairchild in the community and help explain why its culture diffused to the entire semiconductor community and even to cognizant, high technology communities (e.g. venture capital) in the SV and beyond. The founders of Fairchild believed in the culture of democratic community rather than hierarchical workplace. This culture was passed on to spin-off companies of Fairchild and then to their spin-offs, thus spreading the culture of democratic community rather than hierarchical organisation in the SV. This culture, originally an ideal at the organisational level, diffused throughout the community and enabled collaboration across organisational and community boundaries, collective learning, high mobility of labour, informal exchange of information, and so on, all important factors in generating the success the SV has enjoyed in the last few decades. Figure 9 shows the cumulative number of companies in the SV’s semiconductor community from 1960 to 1986. The analysis in the previous section and Figure 9 indicate

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that the development of this community follows the ‘S’ shaped curve proposed by Rogers in his theoretical model of the diffusion of innovations, and by others, such as Assimakopoulos [23] in his work on the emergence of new computer based technological communities. Up to the early 1980s, it seems that at least three stages: innovators, early adopters and early majority can be identified in the evolution of the semiconductor community in the SV. Shockley and Fairchild seem to initiate the innovators stage from 1955 to the early 1960s. After 1965, it is possible to identify the formation of an early adopters category of companies subscribing to this horizontal, community-like, culture originated at Fairchild and nurtured and sustained by the creation of such significant spin offs such as Intel, who grew very rapidly to large corporations, leading to the creation of the critical mass of semiconductor companies in the SV sometime around the mid-1970s. From the mid-1970s to 1980 it seems that the pace of company formation slowed down, and this finding begs for further investigation. The early majority of adopters seem to emerge in the 1980s, though the lack of data from the mid-1980s leaves the picture incomplete with respect to this category.

Figure 9 Cumulative number of companies in the SV’s semiconductor community (1960–1986)

The use of social network methodologies, and in particular the combination of Ucinet and Mage programs, enables us to explore the notion of critical mass and the evolution of this new technological community in some depth. Moreover analyses such as this can also uncover central actors that otherwise might have been missed. For example, the analysis above has repeatedly highlighted the centrality of Intersil Corporation. It was founded in 1967 and within three years it had emerged as a major player in the founding of new companies (see Table 3). And by 1986 it ranked behind only Fairchild, Intel and Hewlett- Packard in terms of degree and betweenness centrality (see Table 6). Seldom, however, do histories of the SV recognise the central role that Intersil seems to have played in the formation of the semiconductor community. Saxenian, for example, never mentions

198 D. Assimakopoulos, S. Everton and K. Tsutsui

Intersil in her account of the SV, and in his historical account of Fairchild, Lécuyer mentions it only in passing, highlighting it as one of the many notable firms that Fairchild employees have helped found over the years [24]. Thus, further research in the evolution of the semi-conductor community is important in understanding the structure and culture of the Silicon Valley as a whole. The process of successive generations of start-ups and the high value placed on entrepreneurship were reinvented in other high-tech industries such as venture capital and helped bring the region its well-documented success.

Acknowledgments

The authors would like to thank Mark Granovetter, Emilio Castilla and Hokyu Hwang at the Department of Sociology, Stanford University, California, for comments on earlier drafts and technical assistance in preparing the social network data for visualisation.

References and Notes 1 Don Hoefler first put together the SV ‘genealogy chart’ in 1971. See, Hoefler, D.C. (1971) ‘Silicon Valley USA’, Electronic News, 11 January, Vol. 1, pp.4–5 and Hoefler, D.C. (1971) ‘Silicon Valley USA Part III’, Electronic News, 25 January, pp.4–5. It was subsequently updated by the trade association, Semiconductor Equipment and Materials International (SEMI). For more information, including instructions of how to order the current poster version, visit http://www.semi.org 2 Freeman, L.C. (2000) ‘Visualizing social networks’, Journal of Social Structure, Vol. 1, No. 1, 4 February, available on-line from http://info.heinz.cmu.edu/project/INSNA/ joss/index1.html 3 See, for example, Scott, J. (2000) Social Network Analysis: A Handbook, 2nd ed., Sage Publications, Thousand Oaks, California and Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge. 4 See, for example, Moreno, J. (1946) ‘Sociogram and Sociomatrix: a note to the paper by Forsyth and Katz’, Sociometry, Vol. 9, pp.348–349. 5 See, for example, Borgatti, S P., Everett, M.G. and Freeman, L.C. (1999) Ucinet 5 for Windows: Software for Social Network Analysis, Analytical Technologies, Natick, Connecticut. Visit http://info.heinz.cmu.edu/project/INSNA/soft_inf.html for a list of computer programs for social network analysis. 6 McGrath, C., Blythe, J. and Krackhardt, D. (1997) ‘The effect of spatial arrangements on judgments and errors in interpreting graphs’, Social Networks, Vol. 19, pp.223–242. 7 See, for example, Freeman, L.C. (1999) ‘Using molecular modeling software in social network analysis: a practicum’, University of California at Irvine, Irvine, California; Retrieved 15 June 2000, from http://eclectic.ss.uci.edu/~lin/chem.html, and Freeman, L.C., Webster, C.M. and Kirke, D.M. (1998) ‘Exploring social structure using dynamic three- dimensional color images’, Social Networks, Vol. 20, pp.109–118. 8 See, for example, Castilla, E., Hwang, H., Granovetter, E. and Granovetter, M. (2000) ‘Social networks in Silicon Valley’ in C.M. Lee, W.F. Miller, M.G. Hancock and H.S. Rowen (Eds.) The Silicon Valley Edge: A Habitat for Innovation and Entrepreneurship, Stanford University Press, Stanford, California, pp.218–247. 9 See, for example, Assimakopoulos, D. (2000) ‘Social network analysis as a tool for understanding the diffusion of GIS innovations’, Environment and Planning B, Vol. 27, pp.627–640.

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10 See Richardson, D. (1999) PC Mage for Windows 95/98/NT, Little River Institute, Durham, North Carolina and Richardson, D. and Richardson, J. (1999) ‘The Kinemage: a tool for scientific communication’, Protein Science, Vol. 1, pp.3–9. Visit http://www.faseb.org/ protein/kinemages/kinpage.html where the program can be downloaded for free. 11 Riordan, M. and Hoddeson, L. (1997) Crystal Fire: The Invention of the Transistor and the Birth of the Information Age, W.W. Norton & Company, New York. 12 Shockley published his employees’ salaries on a bulletin board, he started having them rate one another and he even instigated lie detector tests when he suspected that someone was sabotaging his project. See, Wolfe, T. (1983) ‘The tinkerings of Robert Noyce: how the sun rose on the Silicon Valley’ Esquire, December, pp.346–374. 13 Hoefler, D.C. (1971) ‘Silicon Valley USA’, Electronic News, 11 January, p.4. 14 Kilby was recently awarded the Nobel Prize for this accomplishment. 15 Saxenian, A.L. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128, Harvard University Press, Cambridge, MA. 16 Fairchild sued Baldwin and Rheem claiming that he had stolen Fairchild’s ‘cookbook’ (i.e., its process manual). The court, however, was not terribly impressed with Fairchild’s argument since they essentially did to Shockley what they claimed Rheem did to them. In the end the two companies settled the case out of court whereby Rheem agreed to pay Fairchild $70,000 and to refrain from using one of Fairchild’s proprietary processes. However, while the settlement did not cripple Rheem financially, it did in other ways, and after only two-and-a- half years in business, it was acquired by Raytheon, the company that six years before had refrained from funding Shockley’s business plan. See Hoefler, D.C. (1971) ‘Silicon Valley USA’, Electronic News, 11 January, pp.4–5. 17 Rogers, E.M. (1962) Diffusion of Innovations, 1st ed., The Free Press, New York; and Rogers, E.M. (1983) Diffusion of Innovations, 3rd ed., The Free Press, New York. 18 Valente, T.W. (1995) Network Models of the Diffusion of Innovations, Hampton Press, Creskill, New Jersey. 19 Freeman, C. and Soete, L. (1997) The Economics of Industrial Innovation, 3rd ed., Pinter, London, pp.354–355. See also, Metcalfe, S. (1988) ‘The diffusion of innovation: an interpretative survey’, in G. Dosi, C. Freeman, R. Nelson, G. Silverberg and L. Soete (Eds.) Technical Change and Economic Theory, Pinter, London. 20 For example, Eugene Kleiner, one of Shockley’s ‘traitorous eight’, founded, along with Thomas Perkins, the now top-ranked venture capital firm, Kleiner Perkins Caufield & Byers. 21 Kruskal, J.B. and Wish, M. (1978) Multidimensional Scaling, Sage Publication, Newbury Park, California, pp.49–56; and Scott, [3, p.161]. 22 See Freeman, L.C. (1979) ‘Centrality in social networks I: conceptual clarification’, Social Networks, Vol. 1, pp.215–239, and Wasserman and Faust [3]. 23 See Assimakopoulos, D. (1997) ‘The Greek GIS Community’, Department of Town and Regional Planning, The University of Sheffield, Sheffield, and Assimakopoulos, D. (1997) ‘Social networks and geographic information systems (GIS) diffusion in Greece: disciplinary heterogeneity and constructed advantage within the Greek GIS community’, in C. Capineri and P. Rietveld (Eds.) (1997) Networks in Transport and Communication: A Policy Approach, Ashgate, Aldershot, pp.153–74. 24 Lécuyer, C. (2000) ‘Fairchild semiconductor and its influence’, in C-M. Lee, W.F. Miller, M.G. Hancock and H.S. Rowen (Eds.) The Silicon Valley Edge: A Habitat for Innovation and Entrepreneurship, Stanford University Press, Stanford, California, pp.158–83.