Low Power Programmable Gain Analog to Digital Converter for Integrated Neural Implant Front End
- 611 Downloads
Integrated neural implants interface with the brain using biocompatible electrodes to provide high yield cell recordings, large channel counts and access to spike data and/or field potentials with high signal-to-noise ratio. By increasing the number of recording electrodes, spatially broad analysis can be performed that can provide insights on how and why neuronal ensembles synchronize their activity. However, the maximum number of channels is constrained by noise, area, bandwidth, power, thermal dissipation and the scalability and expandability of the recording system. In this chapter, we characterize the noise fluctuations on a circuit-architecture level for efficient hardware implementation of programmable gain analog to digital converter for neural signal-processing. This approach provides key insight required to address signal-to-noise ratio, response time, and linearity of the physical electronic interface. The proposed methodology is evaluated on a prototype converter designed in standard single poly, six metal 90-nm CMOS process.
KeywordsTotal Harmonic Distortion Noise Voltage Input Transistor Operational Transconductance Amplifier Saturation Voltage
This research was supported in part by the European Union and the Dutch government as part of the CATRENE program under Heterogeneous INCEPTION project.
- 2.Frey, U., et al.: An 11 k-electrode 126-channel high-density micro-electrode array to interact with electrogenic cells. IEEE International Solid-State Circuits Conference Digest of Technical Papers, pp. 158–159 (2007)Google Scholar
- 5.Yin, M., Ghovanloo, M.: A low-noise preamplifier with adjustable gain and bandwidth for bio potential recording applications. In: IEEE International Symposium on Circuits and Systems, pp. 321–324 (2007)Google Scholar
- 10.Seese, T.M., Harasaki, H., Saidel, G.M., Davies, C.R.: Characterization of tissue morphology, angiogenesis, and temperature in the adaptive response of muscle tissue to chronic heating. Lab. Invest. 78(12), 1553–1562 (1998)Google Scholar
- 12.Kölbl, F., et al.: In vivo electrical characterization of deep brain electrode and impact on bio-amplifier design. In: Proceedings of IEEE Biomedical circuits and Systems Conference, pp. 210–213 (2010)Google Scholar
- 15.Rodríguez-Pérez, L., et al.: A 64-channel inductively-powered neural recording sensor array. In: Proceedings of IEEE Biomedical Circuits and Systems Conference, pp. 228–231 (2012)Google Scholar
- 20.Gray, P.R., Meyer, R.G.: Analysis and Design of Analog Integrated Circuits. Wiley, New York (1984)Google Scholar
- 21.Demir, E., Liu, A., Sangiovanni-Vincentelli, A.: Time-domain non-Monte Carlo noise simulation for nonlinear dynamic circuits with arbitrary excitations. In: Proceedings of IEEE International Conference on Computer Aided Design, pp. 598–603 (1994)Google Scholar
- 22.Yang, Z., Zhao, Q., Keefer, E., Liu, W.: Noise characterization, modeling, and reduction for in vivo neural recording. Advances in Neural Information Processing Systems, pp. 2160–2168 (2010)Google Scholar
- 27.Ou, J.: gm/ID based noise analysis for CMOS analog circuits. In: Proceedings of IEEE International Midwest Symposium on Circuits and Systems, pp. 1–4 (2011)Google Scholar
- 28.Song, S., et al.: A 430nW 64nV/VHz current-reuse telescopic amplifier for neural recording application. In: Proceedings of IEEE Biomedical Circuits and Systems Conference, pp. 322–325 (2013)Google Scholar
- 31.Abdelhalim, K., Genov, R.: CMOS DAC-sharing stimulator for neural recording and stimulation arrays. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1712–1715 (2011)Google Scholar