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Machine Vision With InP Based Floating-Gate Photo-Field-Effective Transistors for Color-Mixed Image Recognition

The success of artificial neural networks (ANNs) in machine vision techniques has driven hardware researchers to explore more efficient computing elements for energy-expensive operations such as vector-matrix multiplication (VMM). In this work, InP-based floating-gate photo-field-effective transistors (FG-PFETs) are demonstrated as computing elements that integrate both photodetection and initial signal processing at the sensor level. These devices are fabricated from semiconductor channels grown via a back-end CMOS compatible templated-liquid phase (TLP) approach. Individual devices are shown to exhibit programmable responsivity, mimicking the effect of a synapse connecting the photodetector to a neuron. Using these devices, a simulated optical neural network (ONN) where the experimentally measured performance of FG-PFETs is used as an input shows excellent image recognition accuracy for color-mixed handwritten digits.

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Informasi Detail

Judul Seri

IEEE JOURNAL OF QUANTUM ELECTRONICS; Vol.58 No.4 August 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4600107

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

INDIUM PHOSPHIDE
MACHINE VISION
PHOTOTRANSISTORS
OPTICAL NEURAL NETWORK

Info Detail Spesifik

DOI: 10.1109/JQE.2022.3169565

Pernyataan Tanggungjawab

Jun Tao, Juan Sanchez Vazquez, Hyun Uk Chae, Ragib Ahsan, Rehan Kapadia

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