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Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera

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Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera sensors Article Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera Thomas C. Wilkes 1,*, Andrew J. S. McGonigle 1,2, Tom D. Pering 1, Angus J. Taggart 1, Benjamin S. White 3, Robert G. Bryant 1 and Jon R. Willmott 3 1 Department of Geography, The University of Sheffield, Winter Street, Sheffield S10 2TN, UK; a.mcgonigle@sheffield.ac.uk (A.J.S.M.); t.pering@sheffield.ac.uk (T.D.P.); a.taggart@sheffield.ac.uk (A.J.T.); r.g.bryant@sheffield.ac.uk (R.G.B.) 2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Palermo, via Ugo La Malfa 153, Palermo 90146, Italy 3 Department of Electronic and Electrical Engineering, The University of Sheffield, Portobello Centre, Pitt Street, Sheffield S1 4ET, UK; ben.white@sheffield.ac.uk (B.S.W.); j.r.willmott@sheffield.ac.uk (J.R.W.) * Correspondence: tcwilkes1@sheffield.ac.uk; Tel.: +44-7798-837-894 Academic Editor: Gonzalo Pajares Martinsanz Received: 5 September 2016; Accepted: 3 October 2016; Published: 6 October 2016 Abstract: Here, we report, for what we believe to be the first time, on the modification of a low cost sensor, designed for the smartphone camera market, to develop an ultraviolet (UV) camera system. This was achieved via adaptation of Raspberry Pi cameras, which are based on back-illuminated complementary metal-oxide semiconductor (CMOS) sensors, and we demonstrated the utility of these devices for applications at wavelengths as low as 310 nm, by remotely sensing power station smokestack emissions in this spectral region. Given the very low cost of these units, ≈ USD 25, they are suitable for widespread proliferation in a variety of UV imaging applications, e.g., in atmospheric science, volcanology, forensics and surface smoothness measurements. Keywords: UV camera; UV imaging; smartphone sensor technology; sulphur dioxide emissions; Raspberry Pi; low-cost camera 1. Introduction Ultraviolet (UV) imaging has a wide variety of scientific, industrial and medical applications, for instance in forensics [1], industrial fault inspection [2], astronomy, monitoring skin conditions [3] and in remote sensing [4,5]. To date, scientific grade UV cameras, which have elevated quantum efficiencies in this spectral region, have been applied in this context. However, these systems are relatively expensive (typical unit costs thousands of dollars) and can be power intensive, since they may incorporate thermo-electric cooling. Although these units may provide high signal-to-noise ratios, a lower price point solution could expedite more widespread implementation of UV imaging. Recently, considerable effort has been invested in developing low cost back-illuminated complementary metal-oxide semiconductor (CMOS) sensor technology. Previously, this sensor architecture was applied only within specialist, low light imaging arenas, e.g., in astronomy. In the last few years, however, the manufacturing costs of these devices have been reduced markedly, such that they now feature prominently in consumer electronic products, particularly smartphones. The key advantage of the back-illuminated sensor architecture, over the conventional CMOS configuration, is that the photo-diodes are placed in front of the metal wiring matrix layer of the sensor, thereby improving fill factor and substantially increasing optical throughput to the photoreceptors, particularly in the UV, where these detectors are photosensitive. Sensors 2016, 16, 1649; doi:10.3390/s16101649 www.mdpi.com/journal/sensors Sensors 2016, 16, 1649 2 of 8 Sensors 2016, 16, 1649 2 of 8 To date, however, application of these inexpensive sensors has predominantly been focused on visible imaging,To date, however, due to the application choice of fore-optics of these inexpensive and the Bayer sensors filter has layer predominantly applied to the been sensors, focused in orderon visible imaging, due to the choice of fore-optics and the Bayer filter layer applied to the sensors, in to generate red-green-blue (RGB) mosaics. Recent studies have highlighted that these smartphone order to generate red-green-blue (RGB) mosaics. Recent studies have highlighted that these cameras do have some UV sensitivity [6–8], even with the above optical arrangement. Here, we build smartphone cameras do have some UV sensitivity [6–8], even with the above optical arrangement. on this work by modifying such a camera sensor, to maximize UV throughput to the sensor. To the best Here, we build on this work by modifying such a camera sensor, to maximize UV throughput to the of our knowledge, this constitutes the first report of an imaging system specifically adapted for the sensor. To the best of our knowledge, this constitutes the first report of an imaging system UV, based on a back-illuminated CMOS device developed for the smartphone market. In particular, specifically adapted for the UV, based on a back-illuminated CMOS device developed for the wesmartphone developed amarket UV camera. In particular system,, we based developed on Raspberry a UV ca Pimera camera system, boards, based and on demonstrated Raspberry Pi camera its utility forboards, UV imaging and demonstrated applications its down utility to for 310 UV nm, imaging via a case applications study involving down to remote310 nm, sensing via a case of sulphurstudy dioxideinvolving (SO 2remote) emissions sensing from of sulphur a power dioxide station (SO smokestack.2) emissions from a power station smokestack. 2.2. UV UV Camera Camera Development Development ThisThis work work was was achieved achieved using using Raspberry Raspberry PiPi Camera Module Module v v1.31.3 boards boards (referred (referred to toas asPiCams PiCams hereafter),hereafter), of costof cost≈ USD ≈ USD 25 (Raspberry25 (Raspberry Pi Foundation). Pi Foundation) The. PiCamThe PiCam is based is based on an on Omnivision an Omnivision OV5647 back-illuminatedOV5647 back-illuminated CMOS sensor, CMOS developed sensor, developed primarily forprimarily the mobile for the phone mobile market; phone the market; OV5647 the is a 1/4”OV5647 5-megapixel is a 1/4” (25925-megapixel× 1944 (2592 active × 1944 array) active backlit-CMOS array) backlit unit,-CMOS with unit, 8-/10-bit with 8-/10 RGB/RAW-bit RGB/RAW image output.image PiCamoutput. boards PiCam were boards chosen werehere chosen due here to the due ease to ofthe data ease acquisition of data acquisition and image and processing image usingprocessing Raspberry using Pi Raspberry computer Pi boards computer (Raspberry boardsPi (Raspberry Foundation), Pi Foundation) via the Python, via programmingthe Python language.programming Due to language. their low Due expense to their and low power expense consumption, and power Raspberryconsumption, Pi computers Raspberry arePi computers increasingly beingare increasingly used in a variety being ofused sensing in a variety applications of sensing (e.g., applications [9,10]). Whilst (e.g., this[9,10 work]). Whilst is focused this work on is one sensorfocused type, onpreliminary one sensor type, tests preliminary with another tests such with back-illuminated another such back CMOS-illuminated device, CMOS the Sony device, IMX219 the (SonySony Corporation, IMX219 (Sony Minato, Corporation Tokyo,, Minato Japan),, Tokyo also evidence, Japan), UValso sensitivity.evidence UV sensitivity. TheThe PiCam PiCam lens lens and and filter,filter, housed above above the the sensor sensor within within a plastic a plastic casing casing unit, unit,both absorb both absorb the UV signal, and were therefore removed as a first step to developing the UV camera. This was the UV signal, and were therefore removed as a first step to developing the UV camera. This was achieved chemically using a Posistrip® EKC830TM (DuPont, Wilmington, DE, USA) bath. To improve achieved chemically using a Posistrip® EKC830TM (DuPont, Wilmington, DE, USA) bath. To improve UV sensitivity of the sensor, we then removed the microlens and Bayer filter layers, the latter of UV sensitivity of the sensor, we then removed the microlens and Bayer filter layers, the latter of which which masks the sensor in a mosaic of RGB colour filters (Figure 1), and attenuates much of the masks the sensor in a mosaic of RGB colour filters (Figure1), and attenuates much of the incident UV incident UV radiation. This process effectively turns the PiCam into a monochrome sensor. radiation. This process effectively turns the PiCam into a monochrome sensor. FigureFigure 1. 1.A A schematicschematic ( (CentreCentre)) of of the the Bayer Bayer filter filter array array and andits position its positioninging on the onphotodetector the photodetector array. array.Also Alsoshown shown are aremicroscope microscope images images of the of thePiCam PiCam sensor sensor pre pre-- (Left (Left) and) and post post-- (Right (Right) Bayer) Bayer removalremov process.al process. WhilstWhilst the the filter filter can can be be removed removed byby carefulcareful scratchingscratching (a (a range range of of tools, tools, such such as as metal metal tweezers tweezers oror a pointeda pointed wooden wooden object, object, can can bebe usedused forfor thisthis process),process), a a far far more more uniform uniform finish finish is isachievable achievable chemically;chemically; we we adopted adopte thed the latter latter approach approach using using a five a stepfive step procedure. procedure. We firstWe submergedfirst submerged the sensors the ® TM insensors photoresist in photoresist remover (Posistripremover (Posistrip® EKC830TMEKC830) and heated) and heated (70–100 (70◦C)–100 until °C) the until filter the wasfilter entirely was removed;entirely thisremoved; generally this takesgenerally 10–30 takes minutes, 10–30 dependingminutes, depending on the level on the of appliedlevel of applied heating heating and agitation. and agitation. A second bath of this remover was then applied to optimise the cleaning procedure. A second bath of this remover was then applied to optimise the cleaning procedure. Following this, Following this, the photoresist remover was washed from the sensor by a three stage cleaning the photoresist remover was washed from the sensor by a three stage cleaning procedure, using procedure, using successive baths of n-butyl acetate, acetone and isopropyl alcohol.
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