Retinomorphic devices beyond silicon for dynamic machine vision
Retinomorphic devices beyond silicon for dynamic machine vision
The human visual system can effectively sense optical information through the retina and process it at the visual cortex. Compared with conventional machine vision, it demonstrates superiority in terms of energy efficiency, adaptability, and accuracy. The retina-inspired machine vision systems can process information near or within the sensors at the front end, thereby compressing the raw sensory data and optimising the input to back-end processor for high-level computing tasks. In recent years, amid surge of interest in artificial intelligence technology, research in retinomorphic devices has achieved breakthroughs in both academic and industrial settings. Herein, we present a comprehensive review of this emerging field -based on several materials classes, such as halide perovskites, two-dimensional materials, organic materials and metal oxides. We discuss the steps taken towards achieving not only static pattern recognition, but also dynamic motion tracking and we identify the key challenges that need to be addressed by the community to push this technology forward.
2D materials, metal oxides, neuromorphic, organic materials, perovskites, retinomorphic
Xia, Yuxin
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Babu, Roshni Satheesh
fe7dbe92-1f6b-44e7-94de-4321ff68284a
Vishwanath, Sujaya Kumar
eb9eae3d-23fe-47fa-a24c-65c180df8ef4
Georgiadou, Dimitra G.
84977176-3678-4fb3-a3dd-2044a49c853b
Xia, Yuxin
4bd961bf-9c2d-4c44-8c90-666e32361133
Babu, Roshni Satheesh
fe7dbe92-1f6b-44e7-94de-4321ff68284a
Vishwanath, Sujaya Kumar
eb9eae3d-23fe-47fa-a24c-65c180df8ef4
Georgiadou, Dimitra G.
84977176-3678-4fb3-a3dd-2044a49c853b
Xia, Yuxin, Babu, Roshni Satheesh, Vishwanath, Sujaya Kumar and Georgiadou, Dimitra G.
(2025)
Retinomorphic devices beyond silicon for dynamic machine vision.
Neuromorphic Computing and Engineering, 5 (4), [042001].
(doi:10.1088/2634-4386/ae2156).
Abstract
The human visual system can effectively sense optical information through the retina and process it at the visual cortex. Compared with conventional machine vision, it demonstrates superiority in terms of energy efficiency, adaptability, and accuracy. The retina-inspired machine vision systems can process information near or within the sensors at the front end, thereby compressing the raw sensory data and optimising the input to back-end processor for high-level computing tasks. In recent years, amid surge of interest in artificial intelligence technology, research in retinomorphic devices has achieved breakthroughs in both academic and industrial settings. Herein, we present a comprehensive review of this emerging field -based on several materials classes, such as halide perovskites, two-dimensional materials, organic materials and metal oxides. We discuss the steps taken towards achieving not only static pattern recognition, but also dynamic motion tracking and we identify the key challenges that need to be addressed by the community to push this technology forward.
Text
Xia_2025_Neuromorph._Comput._Eng._5_042001
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Accepted/In Press date: 19 November 2025
e-pub ahead of print date: 5 December 2025
Keywords:
2D materials, metal oxides, neuromorphic, organic materials, perovskites, retinomorphic
Identifiers
Local EPrints ID: 507620
URI: http://eprints.soton.ac.uk/id/eprint/507620
ISSN: 2634-4386
PURE UUID: cc6056ec-aa3c-49b5-b29e-7b3341973709
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Date deposited: 15 Dec 2025 17:56
Last modified: 20 Dec 2025 03:38
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Author:
Yuxin Xia
Author:
Roshni Satheesh Babu
Author:
Sujaya Kumar Vishwanath
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