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Demonstration of a Robust All-Silicon-Carbide Intracortical Neural Interface

Bernardin, Evans K. (author)
Univ S Florida, FL 33620 USA
Frewin, Christopher L. (author)
Univ Texas Dallas, TX 75080 USA
Everly, Richard (author)
Nanotechnol Res and Educ Ctr USF, FL 33617 USA
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Ul-Hassan, Jawad (author)
Linköpings universitet,Halvledarmaterial,Tekniska fakulteten
Saddow, Stephen E. (author)
Univ S Florida, FL 33620 USA
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 (creator_code:org_t)
2018-08-18
2018
English.
In: Micromachines. - : MDPI. - 2072-666X. ; 9:8
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Intracortical neural interfaces (INI) have made impressive progress in recent years but still display questionable long-term reliability. Here, we report on the development and characterization of highly resilient monolithic silicon carbide (SiC) neural devices. SiC is a physically robust, biocompatible, and chemically inert semiconductor. The device support was micromachined from p-type SiC with conductors created from n-type SiC, simultaneously providing electrical isolation through the resulting p-n junction. Electrodes possessed geometric surface area (GSA) varying from 496 to 500 K m(2). Electrical characterization showed high-performance p-n diode behavior, with typical turn-on voltages of 2.3 V and reverse bias leakage below 1 nArms. Current leakage between adjacent electrodes was 7.5 nArms over a voltage range of -50 V to 50 V. The devices interacted electrochemically with a purely capacitive relationship at frequencies less than 10 kHz. Electrode impedance ranged from 675 +/- 130 k (GSA = 496 mu m(2)) to 46.5 +/- 4.80 k (GSA = 500 K mu m(2)). Since the all-SiC devices rely on the integration of only robust and highly compatible SiC material, they offer a promising solution to probe delamination and biological rejection associated with the use of multiple materials used in many current INI devices.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

neural interface; silicon carbide; robust microelectrode

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art (subject category)

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