Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors

Al-Taai, Q. R. , Hejda, M., Zhang, W., Romeira, B., Figueiredo, J. M.L., Wasige, E. and Hurtado, A. (2023) Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors. Neuromorphic Computing and Engineering, 3(3), 034012. (doi: 10.1088/2634-4386/acf609)

Abstract

This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered excitable spike generator. The top contact layer of the device is designed with a nanopillar structure (500 nm in diameter) to restrain the injection current, yielding therefore lower energy operation for spike generation. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs. Moreover, we show the device's ability to deliver spiking responses when biased in either of the two regions adjacent to the negative differential conductance region, the so-called 'peak' and 'valley' points of the current–voltage (I–V) characteristic. This work also demonstrates experimentally key neuron-like dynamical features in the nanostructure RTD-PD, such as a well-defined threshold (in input optical intensity) for spike firing, as well as the presence of spike firing refractory time. The optoelectronic and chip-scale character of the proposed system together with the deterministic, repeatable and well controllable nature of the optically-elicited spiking responses render this nanostructure RTD-PD element as a highly promising solution for high-speed, energy-efficient optoelectronic artificial spiking neurons for novel light-enabled neuromorphic computing hardware.

• Item Type: Articles
• Additional Information: The authors acknowledge support from the European Commission (Grant 828841-ChipAI-H2020- FETOPEN-2018-2020) and by the UK Research and Innovation (UKRI) Turing AI Acceleration Fellowships Program (EP/V025198/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wasige, Professor Edward and Al-Taai, Dr Qusay and Zhang, Dr Weikang
• Authors: Al-Taai, Q. R., Hejda, M., Zhang, W., Romeira, B., Figueiredo, J. M.L., Wasige, E., and Hurtado, A.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Neuromorphic Computing and Engineering
• Publisher: IOP Publishing
• ISSN: 2634-4386
• ISSN (Online): 2634-4386
• Published Online: 21 September 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Neuromorphic Computing and Engineering 3(3): 034012
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.15129/8def9efd-a509-4d5b-aa4e-3abc345680cb

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
303977		ChipAI	Edward Wasige	European Commission (EC)	828841	ENG - Electronics & Nanoscale Engineering