In:
Advanced Functional Materials, Wiley, Vol. 33, No. 5 ( 2023-01)
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.
Type of Medium:
Online Resource
ISSN:
1616-301X
,
1616-3028
DOI:
10.1002/adfm.202212917
Language:
English
Publisher:
Wiley
Publication Date:
2023
detail.hit.zdb_id:
2029061-5
detail.hit.zdb_id:
2039420-2
SSG:
11
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