An ultra low power spiking neural encoder of microwave signals

IF 1.4 4区物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Solid-state Electronics Pub Date : 2024-03-28 DOI:10.1016/j.sse.2024.108910

Christophe Loyez, François Danneville

引用次数: 0

Abstract

In this paper, an original concept is presented in order to perform the spike-based encoding of a Continuous Wave (CW) microwave signal. It relies upon the use of the so-called Morris-Lecar Artificial Neuron (ML AN). It is demonstrated that, when applying the CW microwave signal to the ML AN through a transconductance, spike encoding (with low spike frequency) occurs. It is shown that: (i) the output spike frequency varies as function of the CW microwave voltage signal magnitude, (ii) spike encoding of the microwave signal is observed up to a RF frequency as high as 16 GHz. Thanks to the use of the ML AN to perform this microwave signal encoding, an outstanding ultra low power consumption – less than 100 pW – is achieved.

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微波信号的超低功耗尖峰神经编码器

本文提出了一个新颖的概念，以便对连续波（CW）微波信号进行基于尖峰的编码。它依赖于使用所谓的莫里斯-勒卡人工神经元（ML AN）。实验证明，当通过转导将连续波微波信号应用于 ML AN 时，会出现尖峰编码（尖峰频率较低）。结果表明(i) 输出尖峰频率随 CW 微波电压信号幅度的变化而变化，(ii) 在射频频率高达 16 GHz 时可观察到微波信号的尖峰编码。由于使用了 ML AN 来执行微波信号编码，因此实现了出色的超低功耗（小于 100 pW）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Solid-state Electronics 物理-工程：电子与电气

CiteScore

3.00

自引率

5.90%

发文量

212

审稿时长

3 months

期刊介绍： It is the aim of this journal to bring together in one publication outstanding papers reporting new and original work in the following areas: (1) applications of solid-state physics and technology to electronics and optoelectronics, including theory and device design; (2) optical, electrical, morphological characterization techniques and parameter extraction of devices; (3) fabrication of semiconductor devices, and also device-related materials growth, measurement and evaluation; (4) the physics and modeling of submicron and nanoscale microelectronic and optoelectronic devices, including processing, measurement, and performance evaluation; (5) applications of numerical methods to the modeling and simulation of solid-state devices and processes; and (6) nanoscale electronic and optoelectronic devices, photovoltaics, sensors, and MEMS based on semiconductor and alternative electronic materials; (7) synthesis and electrooptical properties of materials for novel devices.