Broadband Visual Adaption and Image Recognition in a Monolithic Neuromorphic Machine Vision System. (5th December 2022)

• Record Type: Journal Article
• Authors: Cai, Yuchen; Wang, Feng; Wang, Xinming; Li, Shuhui; Wang, Yanrong; Yang, Jia; Yan, Tao; Zhan, Xueying; Wang, Fengmei; Cheng, Ruiqing; He, Jun; Wang, Zhenxing
• Is Part Of: Advanced functional materials. Volume 33:Number 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-05
• Subjects: ferroelectric semiconductors -- long‐term plasticity -- monolithic integrations -- neuromorphic machine vision systems -- visual adaption; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202212917
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Bio‐inspired machine visions have caused wide attentions due to the higher time/power efficiencies over the conventional architectures. Although bio‐mimic photo‐sensors and neuromorphic computing have been individually demonstrated, a complete monolithic vision system has rarely been studied. Here, a neuromorphic machine vision system (NMVS) integrating front‐end retinomorphic sensors and a back‐end convolutional neural network (CNN) based on a single ferroelectric‐semiconductor‐transistor (FST) device structure is reported. As a photo‐sensor, the FST shows a broadband (275–808 nm) retina‐like light adaption function with a large dynamic range of 20.3 stops, and as a unit of the CNN, the FST's weight can be linearly programmed. In total, the NMVS has a high recognition accuracy of 93.0% on a broadband‐dim‐image classification task, which is 20% higher than that of an incomplete system without the retinomorphic sensors. Because of the monolithic unit, the NVMS shows high feasibility for integrated bio‐inspired machine vision systems. Abstract : A monolithic neuromorphic machine vision system (NMVS), consisting of front‐end retinomorphic sensors and a back‐end neuromorphic convolutional neural network, is constructed based on a ferroelectric‐semiconductor‐transistor (FST) device structure. The front‐end FST‐based sensors have a broadband visual adaption capability with a large dynamic range. Combing with a linear weight programming, the NMVS shows a high image recognition accuracy.