[Invite Paper] High Accuracy High Spatial Resolution and Real-Time CMOS Proximity Capacitance Image Sensor Technology and its Applications

ITE Trans. on MTA Vol. 9, No. 2, pp. 122-127 (2021). 122. 1. Introduction. Image sensors can detect and visualize two. dimensional physical quantities in the real world. In the. era of big data, image sensors are expected to be one of. the fundamental technologies that help develop a safe. and sustainable society through its use in various areas. of our daily life. Among various types of image sensors,. proximity capacitance image sensors detect two-. dimensional proximity capacitance information between. sensors and targets. The proximity capacitance discrete. sensors and image sensors have been developed and. employed in various industrial and biometric. applications, such as in fingerprint sensors1-4), human-. machine interface5), liquid level detection for. manufacturing plants, capacitance manometer for gas. pressure detection, non-destructive electrical capacitance. tomography6-7), inspection equipment for flat panel. display and printed circuit boards8). Furthermore,. researches are actively conducted towards visualization. and measurement of cell reactions in the biological and. medical fields9-12).. There are several methodologies for measuring. proximity capacitance: current detection scheme with. small signal inputs1-3), capacitance to frequency. conversion scheme using a ring-oscillator circuit10-11),. and capacitor charge to voltage conversion scheme9). An. atto-farad (aF) order accuracy has been achieved for. discrete proximity capacitance sensors with single or a. few detection channels11, 13-14). For proximity capacitance. image sensors with over 1000 pixels, around 100 aF. accuracy has been achieved16-17). There are many. applications for proximity capacitance image sensors. with micro-meter order pixel pitch with scalable. architecture. In industries they can detect electrical. connections of flat panels, printed circuit boards and. three-dimensional integrated circuit system, and directly. monitor cells in biological field. In addition, real-time. detection capability is also important to detect chemical,. physical and biological reactions through proximity. capacitance imaging.. Recently we have presented proximity capacitance. CMOS image sensors with 0.1 aF detection accuracy. with advanced noise cancelling18-21). In this paper, the. proximity capacitance CMOS image sensor technology. with high detection accuracy, high spatial resolution and. real-time detection and its applications are presented.. The proposed circuit architecture, working principles,. structure and its applications will be described in detail. in the following sections.. Abstract This paper presents a CMOS proximity capacitance image sensor technology achieving 0.1aF. detection accuracy with high spatial resolution with real-time imaging capability for industrial, life science, and. biometric applications. The proposed image sensor circuits, its working principle and device structures are. described in this paper, and additionally, we discuss the foreseen technology roadmap. The fabricated chips with. 16µm pitch pixels achieved a 0.1aF detection accuracy with the input voltage of 20V, thanks to the employed. noise reduction technology. The examples of capacitance imaging using the fabricated CMOS proximity. capacitance image sensor are demonstrated.. Keywords: proximity capacitance, image sensor, noise reduction, inspection equipment. Received March 22, 2021; Accepted March 26, 2021 †1 Graduate School of Engineering, Tohoku University. (Sendai, Japan). †2 New Industry Creation Hatchery Center, Tohoku University (Sendai, Japan). †3 OHT Inc. (Fukuyama, Japan). High Accuracy High Spatial Resolution and Real-Time CMOS Proximity Capacitance Image Sensor Technology and its Applications. Rihito Kuroda†1,†2 (member), Masahiro Yamamoto†1, Yuki Sugama†1, Yoshiaki Watanabe†1, Manabu Suzuki†1, Tetsuya Goto†2, Toshiro Yasuda†3, Shinichi Murakami†3,. Yayoi Yokomichi†3, Hiroshi Hamori†3 and Shigetoshi Sugawa†1,†2 (fellow). Copyright © 2021 by ITE Transactions on Media Technology and Applications (MTA). 2. Proximity capacitance imaging technology. Figure 1 shows the typical cross-sectional diagrams. illustrating the proximity capacitance imaging setups. employed in the proposed technology. The sensor contains. two-dimensional detection electrodes and guard rings in. the top metal layer. The capacitance between measured. targets and the detection electrodes are to be measured.. Here a counter electrode with a supplied input pulse. signal is employed for the measurement. It can be the. conductor target itself as shown in (a), it can be. capacitively coupled with the measurement target as. shown in (b). The proposed technology can be applied to. liquid samples as shown in (c-d), where the counter. electrode can be inserted to the conductive liquid or,. guard ring can be utilized as the counter electrode as in. (d). Here using the configuration in (d), measurement is. conducted without setting an external counter electrode.. Measurements are also available to samples between flat. counter electrode placed on top of the sensor chip and the. measurement target as shown in (e). Figure 1(f). illustrates the measured capacitance distribution for a. conductive target.. Figure 2 shows the simplified sensor circuit and. operational timing diagrams illustrating the working. principle of the developed proximity capacitance image. sensor. The developed technology employs a capacitance. charge to voltage conversion pixel with minimal number. of transistors for scalable architecture. The. measurement capacitance (CS) is in series connection to. the detection capacitance (CC) inside each pixel. The. voltage level of the floating node between CS and CC is. readout via in-pixel source follower circuit like in active. pixel CMOS image sensors. An input pulse (ϕC) is provided to the counter electrode with the voltage. amplitude VIN. When the counter electrode is in the first. voltage level, the floating node is reset to a reset voltage. (VR). After the resetting operation, the thermal noise. remains in the floating node. After resetting, the first. signal is readout (VOUTN). Then, the input pulse is. supplied, and the counter electrode becomes the second. voltage level. The floating node's voltage changes due to. the capacitive coupling between CS and CC. Here the. second signal (VOUTS) is readout through the same in-. pixel SF circuits. The period between the signal readouts. (t0) is typically in several microseconds. By taking a. difference between the two signals (VOUTN and VOUTS),. the thermal noise remained at the floating node, low. frequency noise and the fixed pattern noise due to the. in-pixel SF are suppressed. The following equation. shows the signal associated with this operation,. where GSF is the gain of SF. Note that the detected signal. can be controlled with the VIN, thus a wide range of. capacitance can be measured by adjusting the VIN for the. measured capacitance. In addition, input pulse can. provide more than two voltage states for each frame to. effectively enlarge the dynamic range. Also, the framerate. is controlled independently with the settling time of the. counter electrode, the measurement speed and record. time are flexibly tuned depending on the applications.. Figure 3 shows the layout and cross-sectional diagrams. of the pixel. It contains a detection electrode per pixel and. guard ring is placed in-between the detection electrodes.. For the 16 µm pitch pixels, detection electrode was 12 µm. × 12 µm and the width of guard ring was 1 µm. CC is. (1). 123. Invited PPaper » High Accuracy High Spatial Resolution and Real-Time CMOS Proximity Capacitance Image Sensor Technology and its Applications. Fig. 1 Typical measurement configurations.. Fig. 2 Simplified circuit (left) and operation timing (right). Fig. 3 Pixel layout (left) and cross sectional (right) diagrams.. formed by diffusion capacitance, gate overlap capacitance. and parasitic capacitance of the metal wires, and it is in. the order of femtofarad. The passivation is composed of. 800 nm thick SiO2 and 200 nm thick SiN dielectric films.. On top of the sensor's surface additional passivation such. as polyimide film can be formed to physically protect the. detection electrodes.. Figure 4 shows the circuit diagram of the developed. proximity capacitance image sensor, and the chip. micrograph of the fabricated first prototype chip. A 0.18. µm, 1 poly-Si, 5 metal-layer CMOS image sensor process. was employed for the design and fabrication of the. prototype chips with 16 and 12 µm pitch pixels. For both. pixel types, in-pixel SF uses deep n-wells to isolate its. body, thus the gain of SF circuit becomes nearly unity. In. addition, diffusion of the reset switches and the deep n-. wells forms bipolar protection diodes, it helps to protect. the pixel transistors. Thanks to the simple pixel circuits,. the scaling of the pixel size is available down to at least. 2.8 µm pitch even in the same process technology. With. the shared pixel architecture which is often employed. with CMOS image sensors or using more miniaturized. technology, further scaling down of pixel size is available.. However, decreasing the pixel size leads to the decrease of. detection electrode area which relates to the capacitive. coupling with measured target. Thus, sensitivity needs to. be considered together with the spatial resolution when. designing the pixel size. At this date, a 12 µm pitch. megapixel sensor is ready for mass production, a 5.6 µm. pitch over 4M pixel sensor is to be ready for mass. production in 2021, and a 2.8 µm pitch megapixel sensor. is under development for the next generation proximity. capacitance image sensors.. 3. Results and Discussions. Figure 5 shows the fabricated chips mounted in. ceramic package with bonding wires, and flexible wires. with 20 µ-thick polyimide film on top of the sensor. surface. For the latter configuration, the sensor can be. attached to a sensing target which is larger than the. image sensor itself. Using step and repeat of proximity. capacitance image capturing, imaging of large area. targets such as flat panel displays is available. Figure 6. shows the cross-sectional SEM image of the 16 µm pitch. pixel, showing the detection electrode and guard ring. covered by the passivation film.. Figure 7 shows the images without any measurement. targets obtained with and without the proposed noise. cancelling. The distribution of input referred fixed pattern. noise and temporal random noise are quantitatively. compared for with and without the noise cancelling.. Without noise cancelling, fixed pattern noise due to. threshold variation of in-pixel SF as well as column fixed. pattern noise are obviously large. The temporal random. noise is also large due to the thermal noise arising at the. floating node. These noise components were suppressed. using the proposed noise cancelling technology and the. fixed pattern noise of 56.1 µVrms and temporal noise of. 321 µVrms were successfully obtained at 20MHz signal. sampling frequency without averaging. The sampling. frequency enables a 60 fps operation for the developed. sensor chip with 256H × 256V pixels. Figure 8 shows the. temporal random noise as a function of averaging. number. It becomes 55.1 µVrms at 100 times averaging.. Figure 9 shows the measured transfer characteristics of. the fabricated sensor chip with various measurement. ITE Trans. on MTA Vol. 9, No. 2 (2021). 124. Fig. 6 Cross sectional SEM image of the pixel.. Fig. 5 Chip mounted in a ceramic package with bonding wires. (left) and flexible wires with polyimide film (right).. Fig. 7 Noise characteristics with and without noise cancelling.. Fig. 4 Circuit schematic diagram (left) and chip micrograph (right).. capacitance conditions. Here signal output is plotted as a. function of amplitude of input pulse.. The results indicate that the developed proximity. capacitance CMOS image sensor exhibits a highly linear. response to a wide range of capacitance from 10 fF down. to 0.1 aF. With the obtained characteristics, we can. detect a measured capacitance values knowing the input. pulse amplitude and output signal voltage. In addition,. signal range of about 1.0 V in input referred was. obtained with good linearity.. Figure 10 shows the results of spatial resolution test. measurement using a printed circuit board with Cu wires. with 20 µm lines and 20 µm spaces. Here images were. taken with two conditions; A: only center wire was. provided with a pulse signal while other wires were. grounded and B: center wire was grounded while left and. right surrounding six wires were provided with the input. pulse. This configuration imitates an open failure of a. printed circuit board with dense wires. The results were. compared with a TCAD simulation results using ATLAS,. Silvaco. The obtained results agree with the simulation. results. And we confirmed that the grounded wire in the. center was successfully captured with obtained results.. Several examples of proximity capacitance imaging. are shown as below. Figure 11 shows printed circuit. boards with 200 µm pitch line and space patterns with. or without short and open defects intentionally made.. For the sample with defects, we can clearly detect the. open/short defects as well as their positions. Figure 11. shows surface patterns of a 10-yen coin and a fingerprint. captured with the configuration shown in Fig.1 (e) and. (a) respectively. Fine patterns on the surface were. captured clearly as the difference of distance between. the sensor and the counter electrode. In addition, sweat. pores on the finger are clearly captured thanks to the. high spatial resolution of the developed sensor chip.. Figure 12 shows the proximity capacitance images of a. super bell flower leaf captured with the configuration. shown in Fig.1(e). The left and right images show back. side and front side of the leaf, respectively. Capacitance. values are visualized by contour colors. Distributions of. high water content regions were clearly visualized by. the images, such as along with the veins on the back. side and scattered on the front side. Figure 13 shows an. example of handwriting analysis on papers captured by. configuration shown in Fig.1(e). The effect of trace. 125. Invited PPaper » High Accuracy High Spatial Resolution and Real-Time CMOS Proximity Capacitance Image Sensor Technology and its Applications. Fig. 8 Temporal random noise as a function of averaging number.. Fig. 9 Transfer characteristics with various capacitance conditions. using physiological saline solution and conductive probe. head with various distance. Fig. 10 Spatial resolution test measurement using printed circuit. board.. Fig. 11 Captured images of printed board with 200µm pitch line. and space patterns without and with defects.. pattern as well as ink due to the letters were clearly. visualized, indicating that the developed sensor can be. utilized for authentication methods. Figure 14 shows. capacitance images of the drop of saline solution on top. of the sensors surface captured by the configuration. shown in Fig.1(c), but without input pulse. Here the. images were captured at 30 fps. The process of the. dropped saline solution drying out and remained salt. crystals were visualized clearly, indicating that the. developed sensor can capture capacitance images for. both liquid and solid samples.. Figure 15 shows the benchmarking of the developed. proximity capacitance CMOS image sensor with other. array sensors and discreate capacitance sensors with a few. channels. The developed technology is advantageous with. both high detection accuracy and high spatial resolutions.. For the biological applications visualizing biological cells,. especially higher resolution is required. In addition, for. practical applications such as flat panel display inspection,. a larger imaging area is needed to improve measurement. efficiency. A megapixel sensor chip with 12 µm pitch pixels. are now ready for mass production for this usage. Also, 5.6. µm and 2.8 µm pitch pixels are under development as next. generation sensor chips.. 4. Conclusion. The high precision high spatial resolution and real-. time CMOS proximity capacitance image sensor. technology was described in this paper, and its. performance and examples of applications were. presented. The developed technology is applicable to. various fields and can be utilized for high efficiency. measurement tools.. Acknowledgment. The authors would like to thank LAPIS Semiconductor. for fabrication of the chip, and Y. Itoya and R. Yamazaki. for the measurements of capacitance images of signatures.. References. 1) T. Shimamura, H. Morimura, S. Shigematsu, M. Nakanishi, K. Machida: "Capacitive-sensing circuit technique for image quality improvement on fingerprint sensor LSIs," IEEE J. Solid-State Circ., 45, 5, pp.1080-1087(2010). 2) H. Ma, Z. Liu, S. Heo, J. Lee, K. 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Fig. 16 Pixel pitch and detection accuracy for bench marking with. other sensors.. Fig. 12 Captured images of 10 yen coin (left) and fingerprint (right).. Fig. 13 Captured images of super bell flower leaf.. Fig. 14 Captured images of a paper and the letter A written on a. paper.. capacitance measuring circuits used in tomography systems," Meas. Sci. Technol., Vol.7, pp.1073-1078(1996). 7) D. Chen, W. Yang, M. Pan: "The dynamic response of a Butterworth low-pass filter in an ac-based electrical capacitance tomography system," Meas. Sci. Technol., 21, 105505, pp.1-7(2010). 8) M. Koerdel, F. Alatas, A. Schick, K. Kragler, R.L. Weisfield, S.J. Rupitsch, R. Lerch: "Contactless Inspection of Flat-Panel Displays and Detector Panels by Capacitive Coupling," IEEE Trans. Electron Devices, 58, 10, pp.3453-3462(2011). 9) S.B. Prakash, P. Abshire: "On-Chip Capacitance Sensing for Cell Monitoring Applications," IEEE Sensors J. Vol.7, pp.440-447(2007). 10) B.P. Senevirathna, S. Lu, M.P. Dandin, J. 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Sugawa, T. Goto, R. Kuroda, Y. Sugama, Y. Watanabe, T. Yasuda, H. Hamori: "High Accuracy Proximity Capacitance Image Sensors and Applications Thereof," Keisoku Gijutsu, to be published, June 2021. 127. Invited PPaper » High Accuracy High Spatial Resolution and Real-Time CMOS Proximity Capacitance Image Sensor Technology and its Applications. Shigetoshi Sugawa received the M.S. degree in physics from the Tokyo Institute of Technology, Tokyo, Japan, in 1982 and the Ph.D. degree in electrical engineering from Tohoku University, Sendai, Japan, in 1996. In 1982-1999, he was with Canon Inc. In 1999, he moved to Tohoku University, where he is currently a Professor with the Graduate School of Engineering, also with the New Industry Creation Hatchery Center. ITE fellow.. Yayoi Yokomichi received the B.Ed. degree from Yamaguchi University, Yamaguchi, Japan, in 2000. she joined OHT Inc., Hiroshima, Japan, in 2003, where she is engaged in the development of Non- contact electrical inspection equipment.. Hiroshi Hamori received the Ph.D. degree in engineering from Hiroshima University, Hiroshima, Japan, in 2012. He is a currently president of OHT Inc. that he joined in 1999.. Shinichi Murakami received the B.E. degree in electronic engineering from Hiroshima Institute of Technology, Hiroshima, Japan, in 1979. In 1979-1989, he was with Sharp Corporation. In 1989- 2013, he was with Mitsubishi Heavy Industries Transportation Equipment Engineering & Service Co., Ltd. In 2013, he moved to OHT Inc., Hiroshima, Japan, where he is engaged in the development of Non-contact electrical inspection equipment.. Toshiro Yasuda received the M.S. degree in electronic engineering from Yamaguchi University, Yamaguchi, Japan, in 2006. In 2006-2012, he was with Sanei Hytechs Co., Ltd. In 2012, he moved to OHT Inc., Hiroshima, Japan, where he is engaged in the development of Non-contact electrical inspection equipment.. Manabu Suzuki received the B.S. degree in electrical engineering and the M.S. and Ph.D. degrees in management science and technology from Tohoku University, Sendai, Japan, in 2015, 2017, and 2021, respectively. He was a Research Fellow of the Japan Society for the Promotion of Science Research from 2020 to 2021. Since 2021, he is with the Sony Semiconductor Solutions.. Tetsuya Goto received the B. S., M.S., and Ph.D. degrees in physics from Tsukuba University, Ibaraki, Japan in 1995, 1997, and 2000, respectively. He researched physics of high-temperature plasmas in Tsukuba University. In 2000, he moved to Tohoku University where he is currently a professor in the New Industry Creation Hatchery Center, Tohoku University. He is currently engaged in advanced semiconductor manufacturing, device and process technologies.. Yoshiaki Watanabe received the B.S. degree in electrical engineering from Tohoku University, Sendai, Japan, in 2021, and now pursuing his master course at the graduate school of engineering, Tohoku University. His research includes proximity capacitance image sensor and its applications.. Yuki Sugama received the B.S. degree in electrical engineering from Tohoku University, Sendai Japan in 2020, and now pursuing his master course at the graduate school of engineering, Tohoku University. His research interests include proximity capacitance image sensor and ultra-high speed image sensor technologies. . Masahiro Yamamoto received the B.S. degree in electrical engineering and the M.S. degree in management science and technology from Tohoku University, Sendai, Japan, in 2018, and 2020, respectively. He is now with the Sony Semiconductor Solutions.. Rihito Kuroda received the B.S. degree in electrical engineering and the M.S. and Ph.D. degrees in management science and technology from Tohoku University, Sendai, Japan, in 2005, 2007, and 2010, respectively. He was a Research Fellow of the Japan Society for the Promotion of Science Research from 2007 to 2010. Since 2010, he is with the Graduate School of Engineering, Tohoku University, where he is currently an Associate Professor. ITE member.