An event detection module with a low-power, small-size CMOS image sensor with reference scaling

  • Cheonwi Park
  • Woo-Tae Kim
  • In-June Yeo
  • Moongu Jeon
  • Byung-geun LeeEmail author


This paper presents a low-power and small-size CMOS image sensor (CIS) which can be utilized as a power-efficient event detection system. Since high-resolution images are not required for most event detection purposes, power consumption and chip size of the CIS are optimized only for detection performance. The proposed reference voltage scaling with a multiple input sampling scheme allows the CIS to further minimize power consumption by removing a variable gain amplifier, which is commonly placed in a pixel readout channel. The CIS chip employing a 10 μm-pitch 3T active pixel occupies a die area of 0.98 mm × 0.84 mm. The CIS dissipates 181 μW from 3.0 V analog and 1.4 V digital supplies at the maximum frame rate of 252 fps.


CMOS image sensor Correlated double sampling Event detection Multiple sampling Reference voltage scaling 



This work was supported by the MOTIE Research Grant of 2018 under Grant 10067764. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01433) supervised by the IITP (Institute for Information and Communications Technology Promotion).


  1. 1.
    Hanson, S., Foo, Z., Blaauw, D., & Sylvester, D. (2010). A 0.5 V sub-microwatt CMOS image sensor with pulse-width modulation read-out. IEEE Journal of Solid-State Circuits, 45(4), 759–767. Scholar
  2. 2.
    Yin, C., Chiu, C. F., & Hsieh, C. C. (2016). A 0.5 V, 14.28-kframes/s, 96.7-dB smart image sensor with array-level image signal processing for IoT applications. IEEE Transactions on Electron Devices, 63(3), 1134–1140. Scholar
  3. 3.
    Gottardi, M., Massari, N., & Jawed, S. A. (2009). A 100 mu W 128 × 64 pixels contrast-based asynchronous binary vision sensor for sensor networks applications. IEEE Journal of Solid-State Circuits, 44(5), 1582–1592. Scholar
  4. 4.
    Pardo, F., Boluda, J. A., & Vegara, F. (2015). Selective change driven vision sensor with continuous-time logarithmic photoreceptor and winner-take-all circuit for pixel selection. IEEE Journal of Solid-State Circuits, 50(3), 786–798. Scholar
  5. 5.
    Choi, B.-S., Shin, E., Bae, M., Kim, S.-H., Lee, J., Seo, S.-H., et al. (2017). A low-power CMOS image sensor based on variable frame rate operation. Journal of Semiconductor Technology and Science, 17(6), 854–861. Scholar
  6. 6.
    Choi, J., Shin, J., Kang, D., & Park, D. S. (2016). Always-on CMOS image sensor for mobile and wearable devices. IEEE Journal of Solid-State Circuits, 51(1), 130–140. Scholar
  7. 7.
    Kim, D., Song, M., Choe, B., & Kim, S. Y. (2017). A multi-resolution mode CMOS image sensor with a novel two-step single-slope ADC for intelligent surveillance systems. Sensors (Basel), 17(7), 1497. Scholar
  8. 8.
    Zou, Y., Gottardi, M., Perenzoni, D., Perenzoni, M., & Stoppa, D. (2017). A 1.6 mW 320 × 240-pixel vision sensor with programmable dynamic background rejection and motion detection. In 2017 IEEE sensors (pp. 1–3).
  9. 9.
    Kumagai, O., Niwa, A., Hanzawa, K., Kato, H., Futami, S., Ohyama, T., et al. (2018). A 1/4-inch 3.9 Mpixel low-power event-driven back-illuminated stacked CMOS image sensor. In 2018 IEEE international solidstate circuits conference(ISSCC) (pp. 86–88).
  10. 10.
    Jo, Y. R., Hong, S. K., & Kwon, O. K. (2016). A low-noise and area-efficient PWM-Delta Sigma ADC using a single-slope quantizer for CMOS image sensors. IEEE Transactions on Electron Devices, 63(1), 168–173. Scholar
  11. 11.
    Yasue, T., Kitamura, K., Watabe, T., Shimamoto, H., Kosugi, T., Watanabe, T., et al. (2016). A 1.7-in, 33-Mpixel, 120-frames/s CMOS image sensor with depletion-mode MOS Capacitor-based 14-b two-stage cyclic A/D converters. IEEE Transactions on Electron Devices, 63(1), 153–161. Scholar
  12. 12.
    Maddalena, L., & Petrosino, A. (2008). A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing, 17(7), 1168–1177. Scholar
  13. 13.
    Cucchiara, R., Grana, C., Piccardi, M., & Prati, A. (2003). Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1337–1342. Scholar
  14. 14.
    Zivkovic, Z., & van der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, 27(7), 773–780. Scholar
  15. 15.
    Song, Y., Noh, S., Yu, J., Park, C., & Lee, B. (2014). Background subtraction based on Gaussian mixture models using color and depth information. In The 2014 international conference on control, automation and information sciences (ICCAIS 2014) (pp. 132–135).
  16. 16.
    Ay, S. U. (2013). Boosted CMOS APS pixel readout for ultra low-voltage and low-power operation. IEEE Transactions on Circuits and Systems II-Express Briefs, 60(6), 341–345. Scholar
  17. 17.
    Chye Huat, A., & Wooley, B. A. (1996). A 128 × 128-pixel standard CMOS image sensor with electronic shutter. In 1996 IEEE international solid-state circuits conference. Digest of Technical Papers, ISSCC (pp. 180–181).
  18. 18.
    Tang, F., Chen, D. G., Wang, B., & Bermak, A. (2013). Low-power CMOS image sensor based on column-parallel single-slope/SAR quantization scheme. IEEE Transactions on Electron Devices, 60(8), 2561–2566. Scholar
  19. 19.
    Liang, C. K., Chang, L. W., & Chen, H. H. (2008). Analysis and compensation of rolling shutter effect. IEEE Transactions on Image Processing, 17(8), 1323–1330. Scholar
  20. 20.
    Han, S. W., & Yoon, E. (2006). Area-efficient correlated double sampling scheme with single sampling capacitor for CMOS image sensors. Electronics Letters, 42(6), 335–337. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceGwangju Institute of Science and Technology (GIST)GwangjuRepublic of Korea

Personalised recommendations