arrow
Return

An ultra-low-power programmable analog bionic ear processor

delete2005-04-01
delete163
PRE
AI
R
Rahul Sarpeshkar
C
Christopher Salthouse
J
Ji‐Jon Sit
B
Baker, MW
S
Serhii M. Zhak
L
Lu, TKT
L
Lorenzo Turicchia
S
S. Balster
DOI:10.1109/TBME.2005.844043delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
We report a programmable analog bionic ear (cochlear implant) processor in a 1.5-mu m BiCMOS technology with a power consumption of 211 mu W and 77-dB dynamic range of operation. The 9.58 mm x 9.23 mm processor chip runs on a 2.8 V supply and has a power consumption that is lower than state-of-the-art analog-to-digital (A/D)-then-DSP designs by a factor of 25. It is suitable for use in fully implanted cochlearimplant systems of the future which require decades of operation on a 100-mAh rechargeable battery with a finite number of charge-discharge cycles. It may also be used as an ultra-low-power spectrum-analysis front end in portable speech-recognition systems. The power consumption of the processor includes the 100 mu W power consumption of a JFET-buffered electret microphone and an associated on-chip microphone front end. An automatic gain control circuit compresses the 77-dB input dynamic range into a narrower internal dynamic range (IDR) of 57 dB at which each of the 16 spectral channels of the processor operate. The output bits of the processor are scanned and reported off chip in a format suitable for continuous-interleaved-sampling stimulation of electrodes. Power-supply-immune biasing circuits ensure robust operation of the processor in the high-RF-noise environment typical of cochlear implant systems.
Keywords:
bionic ear
cochlear implant
hearing aids
low power
spectrum analysis
speech recognition
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE Transactions on Biomedical Engineering
IF:
4.5
Papers:
573
Citations:
2.8W

Organization

No organization information available
Cited Papers

Cited Papers

Toward reliable RRAM performance: macro- and micro-analysis of operation processes
err2017-11-09
err0
PREAI
errGennadi Bersuker; Dmitry Veksler; David M. Nminibapiel; Pragya R. Shrestha; Jason P. Campbell; Jason T. Ryan; Helmut Baumgart; Maribeth S. Mason; Kin P. Cheung
errShare
errSave
errShare
errSave
errShare
errSave
The Melting of Amorphous Si
err2011-02-26
err0
PREAI
errJ. M. Poate; P. S. Peercy; M. O. Thompson
errShare
errSave
researcher View more