A low-power low-noise amplifier with fully self-biased feedback loop structure for neural recording

  • Xianzhe Zhang
  • Jingyu WangEmail author
  • Zhangming Zhu
Mixed Signal Letter


This paper presents a fully self-biased, low-power LNA for neural recordings. Due to the capacitor coupling and low-supply voltage in LNA, the appropriate dc bias voltages for saturating the input transistors NMOS and PMOS of LNA should be separately provided. This paper focuses on the effects of different feedback ways to obtain input dc bias voltage, and proposes a completely self-biased structure to obtain the bias voltage directly from the inner nodes of the circuit. This connected way avoids using extra dc biasing circuit totally, saves capacitor area effectively and reduces the high-pass corner frequency greatly. Furthermore, the proposed method eliminates the likelihood for initial DC latch-up in the traditional way, making the circuit more stable. Simulated in a 0.18-µm CMOS process, the LNA consumes 1.2 µA from a 0.6 V supply, and achieves an input referred noise of 4.98 µVrms (1–10 kHz), corresponding to a noise efficiency factor of 2.13. Simulated CMRR and THD exceed 77 dB and 75 dB, separately.


Fully self-biased feedback LNA Neural recordings 



Funding was provided by National Natural Science Foundation of China (Grant Nos. 61804110 and 61625403).


  1. 1.
    Tao, W., Li, F., Wang, C., Tu, G., Wang, Z., & Lü, Xiaoying. (2017). Design of integrated neural stimulating and recording frontend for bladder control prosthesis. Analog Integrated Circuits and Signal Processing, 91(3), 403–416.CrossRefGoogle Scholar
  2. 2.
    Miguez, M. R., Gak, J., Arnaud, A., Oliva, A. R., & Julián, Pedro. (2018). A current-reuse biomedical amplifier with a NEF < 1. Analog Integrated Circuits and Signal Processing, 95, 283–294.CrossRefGoogle Scholar
  3. 3.
    Lopez, C. M. et al. (2016). A 966-electrode neural probe with 384 configurable channels in 0.13 μm SOI CMOS. In IEEE international solid-state circuits conference (ISSCC) digest of technical papers (pp. 392–393).Google Scholar
  4. 4.
    Jochum, T., Denison, T., & Wolf, P. (2009). Integrated circuit amplifiers for multi-electrode intracortical recording. Journal of Neural Engineering, 6(1), 012001.CrossRefGoogle Scholar
  5. 5.
    Park, S. Y., Cho, J., Na, K., & Yoon, E. (2018). Modular 128-channel ∆ − ∆Σ analog front-end architecture using spectrum equalization scheme for 1024-channel 3-D neural recording microsystems. IEEE Journal of Solid-State Circuits, 53(2), 501–514.CrossRefGoogle Scholar
  6. 6.
    Stevenson, I. H., & Kording, K. P. (2011). How advances in neural recording affect data analysis. Nature Neuroscience, 14(2), 139.CrossRefGoogle Scholar
  7. 7.
    Chen, Y. P., et al. (2015). An injectable 64 nW ECG mixed-signal SoC in 65 nm for arrhythmia monitoring. IEEE Journal of Solid-State Circuits, 50(1), 375–390.CrossRefGoogle Scholar
  8. 8.
    Song, S., Rooijakkers, M., Harpe, P., Rabotti, C., Mischi, M., Van Roermund, A. H. M., et al. (2015). A low-voltage chopper-stabilized amplifier for fetal ECG monitoring with a 1.41 power efficiency factor. IEEE Transactions on Biomedical Circuits and Systems, 9(2), 237–247.CrossRefGoogle Scholar
  9. 9.
    Denison, T., Consoer, K., Santa, W., Avestruz, A. T., Cooley, J., & Kelly, A. (2007). A 2 μW 100 nV/rtHz chopper-stabilized instrumentation amplifierfor chronic measurement of neural field potentials. IEEE Journal of Solid-StateCircuits, 42(12), 2934–2945.CrossRefGoogle Scholar
  10. 10.
    Chang, S.-I., Park, S.-Y., & Yoon, E. (2017). Low-power low-noise pseudoopen-loop preamplifier for neural interfaces. The IEEE Sensors Journal, 17(15), 4843–4852.CrossRefGoogle Scholar
  11. 11.
    Yang, T., & Holleman, J. (2015). An ultra low-power low-noise CMOS biopotential amplifier for neural recording. IEEE Transactions on Circuits and Systems II: Express Briefs, 62(10), 927–931.CrossRefGoogle Scholar
  12. 12.
    Zhang, F., Holleman, J., & Otis, B. P. (2012). Design of ultra-low power biopotential amplifiers for biosignal acquisition applications. IEEE Transactions on Biomedical Circuits and Systems, 6(4), 344–355.CrossRefGoogle Scholar
  13. 13.
    Chandrakumar, H., & Markovic, D. (2017). A high dynamic-range neural recording chopper amplifier for simultaneous neural recording and stimulation. The IEEE Journal of Solid-State Circuits, 52(3), 645–656.CrossRefGoogle Scholar
  14. 14.
    Harrison, R. R., & Charles, C. (2003). A low-power low-noise CMOS amplifier for neural recording applications. The IEEE Journal of Solid-State Circuits, 38(6), 958–965.CrossRefGoogle Scholar
  15. 15.
    Van Helleputte, N., Kim, S., Kim, H., Kim, J. P., Van Hoof, C., & Yazicioglu, R. F. (2011). A 160 μW 8-channel active electrode system for EEG monitoring. IEEE Transactions on Biomedical Circuits and Systems, 5(6), 555–567.CrossRefGoogle Scholar
  16. 16.
    Chandrakumar, H., & Markovic, D. (2017). An 80-mVpp linear-input range, 1.6-GΩ input impedance, low-power chopper amplifier for closed-loop neural recording that is tolerant to 650-mvpp common-mode interference. IEEE Journal of Solid-State Circuits, 52(11), 2811–2828.Google Scholar
  17. 17.
    Zhang, J., Zhang, H., Sun, Q., & Zhang, R. (2018). A low-noise, low-power amplifier with current-reused OTA for ECG recordings. IEEE Transactions on Biomedical Circuits & Systems, PP(99), 1–9.Google Scholar
  18. 18.
    Fan, Q., Sebastiano, F., Huijsing, J. H., et al. (2011). A 1.8 W 60 nV Hz capacitively-coupled chopper instrumentation amplifier in 65 nm CMOS for wireless sensor nodes. IEEE Journal of Solid-State Circuits, 46(7), 1534–1543.CrossRefGoogle Scholar
  19. 19.
    Müller, R. et al. (2014). A miniaturized 64-channel 225 μW wireless electrocorticographic neural sensor. In IEEE international solid-state circuits conference-(ISSCC) digest of technical papers, San Francisco, CA, USA, (pp. 412–413).Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Microelectronics, Xidian UniversityXi’anChina

Personalised recommendations