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A Low Power and Wide Dynamic Range Digital Pixel Sensor (DPS) for Optical Brain Imaging Systems

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Abstract

This paper proposes a self-reset pulse frequency modulation (PFM) digital pixel sensor (DPS) with in-pixel variable reference voltage for optical brain imaging systems. The sensor demonstrates a wide dynamic range and very low power consumption that can detect small signals of brain activity in brain. The high dynamic range, high SNR (signal-to-noise ratio), high speed and low power consumption image sensor are suitable for optical brain imaging systems. Since the comparator part consumes high power inside pixel, sub-threshold, self-biased and bulk-driven techniques are used to achieve both ultra-low-voltage and low power in the PFM DPS. Moreover, High speed (high frame rate) is achieved by image capturing in-parallel for all pixels. The proposed image sensor is post layout simulated in 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology with 0.6 V supply voltage, resulting in the dynamic range of 152 dB and the power consumption of 11.25 nW and the fill factor of the proposed sensor is 11%. Hence, this device has significant potential to be used for brain signal detection in pre-clinical and clinical studies, cognitive process, diagnose diseases in exploring brain structure and function.

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References

  1. Asbury, C. (2011). Brain imaging technologies and their applications in neuroscience. The Dana foundation, pp 1–45.

  2. Khiarak, M. N., Gagnon-Turcotte, G., Martianova, E., Martel, C. B. S., Proulx, C. D., & De Koninck et al. (2018). A Wireless Optoelectronic Neuroscience Platform for Chronic Fluorescence Sensing in Freely Behaving Rodents. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology society (EMBC) (pp.1608–1611). IEEE.

  3. Khiarak, M. N., Sasagawa, K., Tokuda, T., Ohta, J., Martel, S., & De Koninck et al. (2018). An Energy-Efficient CMOS Biophotometry Sensor With Incremental DT-∑ Δ ADC Conversion. In 2018 IEEE International Symposium on Circuits and systems (ISCAS) (pp.1–4). IEEE.

  4. Khiarak, M. N., Sasagawa, K., Tokuda, T., Ohta, J., Martel, S., & De Koninck, Y. et al. (2018). Live Demonstration: An Energy-Efficient CMOS Biophotometry Sensor Interface. In 2018 IEEE International Symposium on Circuits and systems (ISCAS) (pp.1–1). IEEE.

  5. Khiarak, M. N., Martel, S., De Koninck, Y., & Gosselin, B. (2018). Wireless Optoelectronic Fiber Photometry Headstage for Deep Brain Structures Monitoring.In 2018 IEEE Life Sciences Conference (LSC) (pp.9–12). IEEE.

  6. Khiarak, M. N., Sasagawa, K., Tokuda, T., Ohta, J., Martel, S., De Koninck, Y., & Gosselin, B. (2018). A 17-bit 104-dB-DR high-precision low-power CMOS fluorescence biosensor with extended counting ADC and noise cancellation. In 2018 16th International New Circuits and Systems Conference (NEWCAS) (pp.100–103). IEEE.

  7. Srilahari, N., Satyanarayana, N., Sunitha, P., Uma Sanker, A., Bala Sundaram, M., & Sobana, R Non-Invasive Functional Optical Brain Imaging Methods: A Review.

  8. Haruta, M., Sunaga, Y., Yamaguchi, T., Takehara, H., Noda, T., Sasagawa, K., Tokuda, T., & Ohta, J. (2015). Intrinsic signal imaging of brain function using a small implantable CMOS imaging device. Japanese Journal of Applied Physics, 54(4S), 04DL10.

  9. Tagawa, A., Mitani, M., Minami, H., Noda, T., Sasagawa, K., Tokuda, T., & Ohta, J. (2010). Complementary Metal Oxide Semiconductor Based Multimodal Sensor for In vivo Brain Function Imaging with a Function for Simultaneous Cell Stimulation. Japanese Journal of Applied Physics,49(4S), 04DL02.

  10. Vatteroni, M., Covi, D., Stoppa, D., Crespi, B., & Sartori, A. (2007). High dynamic range CMOS image sensors in biomedical applications. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2819–2822). IEEE.

  11. Zhang, X., Noor, M. S., McCracken, C. B., Kiss, Z. H., Yadid-Pecht, O., & Murari, K. (2015). CMOS image sensor and system for imaging hemodynamic changes in response to deep brain stimulation. IEEE Transactions on Biomedical and Circuits and Systems, 10(3), 632–642.

    Article  Google Scholar 

  12. Sasagawa, K., Yamaguchi, T., Haruta, M., Sunaga, Y., Takehara, H., et al. (2015). An implantable CMOS image sensor with self-reset pixels for functional brain imaging. IEEE Transactions on Electron Devices, 63(1), 215–222.

    Article  Google Scholar 

  13. Yamaguchi, T., Takehara, H., Sunaga, Y., Haruta, M., Motoyama, M., Ohta, Y., Noda, T., Sasagawa, K., Tokuda, T., & Ohta, J. (2016). Implantable self-reset CMOS image sensor and its application to hemodynamic response detection in living mouse brain. Japanese Journal of Applied Physics, 55(4S), 04EM02.

  14. Pakpuwadon, T., Sasagawa, K., Christian Guinto, M., Ohta, Y., Haruta, M., Takehara, H., Tashiro, H., & Ohta, J. (2021). Self-reset image sensor with a signal-to-noise ratio over 70 dB and its application to brain surface imaging. Frontiers in Neoroscience, 15, 714.

  15. Khiarak, M. N., Martel, S., De Koninck, Y., & Gosselin, B. (2017). A high-sensitivity CMOS biophotometry sensor with embedded continuous-time ΣΔ modulation.In 2017 IEEE International Symposium on Circuits and Systems (ISCAS) (pp.1–4). IEEE.

  16. Moeys, D. P., Corradi, F., Li, C., Bamford, S. A., Longinotti, L., Voigt, F. F., Berry, S., Taverni, G., Helmchen, F., & Delbruck, T. (2017). A sensitive dynamic and active pixel vision sensor for color or neural imaging applications. IEEE Transactions on Biomedical and Circuits and Systems, 12(1), 123–136.

    Article  Google Scholar 

  17. Chen, Y., Yuan, F., & Khan, G. (2009). A wide dynamic range CMOS PFM digital pixel sensor with in-pixel variable voltage reference. Analog Integrated Circuits and Signal Processing, 61(3), 287.

    Article  Google Scholar 

  18. Hirsch, S., et al. (2019). Realization and opto-electronic characterization of linear self-reset pixel cells for a high dynamic CMOS image sensor. Advances in Radio Science, 17(2019), 239–247.

    Article  Google Scholar 

  19. Hassanli, K., Sayedi, S. M., & Jacob Wikner, J. (2017). High resolution digital imager based on time multiplexing algorithm. IEEE Sensors Journal, 17(9), 2831–2840.

    Article  Google Scholar 

  20. Hassanli, K., Sayedi, S. M., & Jacob Wikner, J. (2016). A compact, low-power, and fast pulse-width modulation based digital pixel sensor with no bias circuit. Sensors and Actuators A: Physical, 244, 243–251.

    Article  Google Scholar 

  21. Zhiyuan, Y. Z., & Jiangtao, Xu. (2017). Multi-ramp reference voltage PWM imaging scheme with enhanced dynamic range and correlated double sampling. Microelectronic Journal, 69(2017), 91–100.

    Google Scholar 

  22. Köklü, G., Sun, D., Leblebici, Y., De Micheli, G., & Carrara, S. (2013). An event-detection high dynamic range CMOS Image Sensor. In SENSORS,2013 IEEE(pp.1–4).IEEE.

  23. Vargas-Sierra, S., Liñán-Cembrano, G., & Rodríguez-Vázquez, Á. (2014). A 151 dB high dynamic range CMOS image sensor chip architecture with tone mapping compression embedded in-pixel. IEEE Sensors Journal, 15(1), 180–195.

    Article  Google Scholar 

  24. Hassanli, K., et al. (2015). A highly sensitive, low-power, and wide dynamic range CMOS digital pixel sensor. Sensors and Actuators A: Physical, 236, 82–91.

    Article  Google Scholar 

  25. Sarkar, Mukul, & Albert Theuwissen. (2012) A biologically inspired CMOS image sensor. Springer.

  26. Snaevarsdottir, F. (2018). CMOS Image Sensor Design Methodology Applied to Optical Tomography and Neural Networks.

  27. Yung, Y. F. (2004). Digital pixel sensor(DPS) array based on pulse width modulation(PWM) scheme (Doctoral dissertation).

  28. Bermak, A., Abdessellam Bouzerdoum, & Kamran Eshraghian (2002) A vision sensor with on-pixel ADC and in-built light adaptation mechanism. Microelectronics Journal, 33(12), 1091–1096.

  29. Chen, W. K. (Ed.).(2003).Analog circuits and devices. CRC Press.

  30. Mori, M., et al. (2016) An APD-CMOS image sensor toward high sensitivity and wide dynamic range. In 2016 IEEE International Electron Devices Meeting (IEDM). IEEE.

  31. Ghormishi, M. H., & Karami, M. A. (2015). Design and optimization of backside illuminated image sensor for epiretinal implants. Computers and Electrical Engineering, 45, 352–358.

  32. Millet, L., Vigier, M., Sicard, G., Uhring, W., Margotat, N., Guellec, F., & Martin, S. (2018). A 5 Million Frames Per Second 3D Stacked Image Sensor With In-Pixel Digital Storage. In ESSCIRC 2018-IEEE 44th European Solid State Circuits Conference (ESSCIRC) (pp.62–65). IEEE.

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Correspondence to Yasin Salehifar.

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Salehifar, Y., Ayatollahi, A. & Karami, M.A. A Low Power and Wide Dynamic Range Digital Pixel Sensor (DPS) for Optical Brain Imaging Systems. Sens Imaging 23, 4 (2022). https://doi.org/10.1007/s11220-021-00373-z

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  • DOI: https://doi.org/10.1007/s11220-021-00373-z

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