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Analysing and measuring the performance of memristive integrating amplifiers

Analysing and measuring the performance of memristive integrating amplifiers
Analysing and measuring the performance of memristive integrating amplifiers

Recording reliably extracellular neural activities is an essential prerequisite for the development of bioelectronics and neuroprosthetic applications. Recently, a fully differential, two-stage, integrating pre-amplifier was proposed for amplifying and then digitising neural signals. The amplifier featured a finely tuneable offset that was used as a variable threshold detector. Given that the amplifier is integrating, the DC operating point keeps changing during integration, rendering traditional analysis (AC/DC) unsuitable. In this work, we analyse the operation of this circuit and propose alternative definitions for validating the necessary key performance metrics, including gain, bandwidth, offset tuning range and offset sensitivity with respect to the memory states of the employed memristors. The amplification process is analysed largely through investigating the transient behaviour during the integration phase. This benchmarking approach is finally leveraged for providing useful insights and design trade-offs of the memristor-based integrating amplifier.

high sensitivity, hybrid CMOS/memristor circuit, integrating amplifier, neural spike detection, threshold detection
0098-9886
3507-3525
Wang, Jiaqi
8b0d7a69-fc27-4344-ab3d-9f05fba98145
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Papavassiliou, Christos
86fe7042-20a3-47a9-9430-2bdb6c260303
Maheshwari, Sachin
f09ac1de-0e3d-410d-a7e2-f4d54a1459b9
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Wang, Jiaqi
8b0d7a69-fc27-4344-ab3d-9f05fba98145
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Papavassiliou, Christos
86fe7042-20a3-47a9-9430-2bdb6c260303
Maheshwari, Sachin
f09ac1de-0e3d-410d-a7e2-f4d54a1459b9
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Wang, Jiaqi, Serb, Alexantrou, Papavassiliou, Christos, Maheshwari, Sachin and Prodromakis, Themistoklis (2021) Analysing and measuring the performance of memristive integrating amplifiers. International Journal of Circuit Theory and Applications, 49 (11), 3507-3525. (doi:10.1002/cta.3101).

Record type: Article

Abstract

Recording reliably extracellular neural activities is an essential prerequisite for the development of bioelectronics and neuroprosthetic applications. Recently, a fully differential, two-stage, integrating pre-amplifier was proposed for amplifying and then digitising neural signals. The amplifier featured a finely tuneable offset that was used as a variable threshold detector. Given that the amplifier is integrating, the DC operating point keeps changing during integration, rendering traditional analysis (AC/DC) unsuitable. In this work, we analyse the operation of this circuit and propose alternative definitions for validating the necessary key performance metrics, including gain, bandwidth, offset tuning range and offset sensitivity with respect to the memory states of the employed memristors. The amplification process is analysed largely through investigating the transient behaviour during the integration phase. This benchmarking approach is finally leveraged for providing useful insights and design trade-offs of the memristor-based integrating amplifier.

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Accepted/In Press date: 22 May 2021
e-pub ahead of print date: 12 July 2021
Published date: 14 November 2021
Keywords: high sensitivity, hybrid CMOS/memristor circuit, integrating amplifier, neural spike detection, threshold detection

Identifiers

Local EPrints ID: 452123
URI: http://eprints.soton.ac.uk/id/eprint/452123
ISSN: 0098-9886
PURE UUID: ce96df08-a79c-439a-8ea1-7f2ed349fb25
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

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Date deposited: 25 Nov 2021 16:46
Last modified: 16 Mar 2024 14:36

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Contributors

Author: Jiaqi Wang
Author: Alexantrou Serb
Author: Christos Papavassiliou
Author: Sachin Maheshwari
Author: Themistoklis Prodromakis ORCID iD

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