Redundant Crossfire: A Technique to Achieve Super-Resolution in Neurostimulator Design by Exploiting Transistor Mismatch

Anh Tuan Nguyen, Jian Xu, Diu Khue Luu, Cynthia K. Overstreet, Jonathan Cheng, Edward W. Keefer, Zhi Yang

Research output: Contribution to journalArticlepeer-review

Abstract

A high-resolution neurostimulator is the essential component of many bidirectional neural interfaces. In practice, the effective resolution of fully integrated neurostimulator designs is often hindered by the transistor mismatch, especially in submicrometer CMOS processes. In this article, we present a new circuit technique called redundant crossfire (RXF) to address this challenge. It is derived from our redundant sensing (RS) framework, which aims at engineering information redundancy into the system architecture to enhance its effective resolution. RXF involves combining (i.e., crossfiring) the outputs of two or more current drivers to form a redundant structure that, when properly configured, can produce accurate current pulses with an effective super-resolution beyond the limitation commonly permitted by the physical constraints. Unlike any previous works, the proposed technique achieves high-accuracy by directly exploiting the random transistor mismatch with an excessively large mismatch ratio of 10%-20%. The effectiveness of RXF is verified through both Monte Carlo simulations and measurement results of a fully integrated neurostimulator chip. Equipped with a 5-bit current digital-to-analog converter (IDAC) and two 4-bit current multipliers, the stimulator achieves an effective resolution of 9.75 bits in a 1.1-mA full range. An application of the fabricated chip is to deliver neuro-feedback to a human amputee through peripheral nerves where the amplitude of stimulation pulses is accurately controlled to encode the tactile response's intensity.

Original languageEnglish (US)
Article number9355574
Pages (from-to)2452-2465
Number of pages14
JournalIEEE Journal of Solid-State Circuits
Volume56
Issue number8
DOIs
StatePublished - Aug 2021

Bibliographical note

Funding Information:
Manuscript received August 21, 2020; revised November 12, 2020 and December 28, 2020; accepted January 27, 2021. Date of publication February 17, 2021; date of current version July 23, 2021. This article was approved by Associate Editor Nick van Helleputte. This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) under Grant HR0011-17-2-0060 and Grant N66001-15-C-4016, in part by internal funding from the University of Minnesota, in part by NIH under Grant R21-NS111214, in part by the NSF CAREER under Award 1845709, and in part by Fasikl Inc. (Corresponding author: Zhi Yang.) Anh Tuan Nguyen and Zhi Yang are with the Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA, and also with Fasikl Inc., Minneapolis, MN 55113 USA (e-mail: yang5029@umn.edu).

Publisher Copyright:
© 1966-2012 IEEE.

Keywords

  • Bidirectional neural interface
  • high-resolution
  • human-machine interface
  • neuro-feedback
  • neuroprosthesis
  • neurostimulator
  • redundant crossfire (RXF)
  • redundant sensing (RS)
  • super-resolution
  • transistor mismatch

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