Fully Light-Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Fully Light-Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

© 2020 Wiley-VCH GmbH.

Détails bibliographiques
Publié dans:Advanced materials (Deerfield Beach, Fla.). - 1998. - 33(2021), 10 vom: 06. März, Seite e2004207
Auteur principal: Ahmed, Taimur (Auteur)
Autres auteurs: Tahir, Muhammad, Low, Mei Xian, Ren, Yanyun, Tawfik, Sherif Abdulkader, Mayes, Edwin L H, Kuriakose, Sruthi, Nawaz, Shahid, Spencer, Michelle J S, Chen, Hua, Bhaskaran, Madhu, Sriram, Sharath, Walia, Sumeet
Format: Article en ligne
Langue:English
Publié: 2021
Accès à la collection:Advanced materials (Deerfield Beach, Fla.)
Sujets:Journal Article artificial neural networks black phosphorus machine learning neuromorphics optical memory
Description
Résumé:© 2020 Wiley-VCH GmbH.
Imprinting vision as memory is a core attribute of human cognitive learning. Fundamental to artificial intelligence systems are bioinspired neuromorphic vision components for the visible and invisible segments of the electromagnetic spectrum. Realization of a single imaging unit with a combination of in-built memory and signal processing capability is imperative to deploy efficient brain-like vision systems. However, the lack of a platform that can be fully controlled by light without the need to apply alternating polarity electric signals has hampered this technological advance. Here, a neuromorphic imaging element based on a fully light-modulated 2D semiconductor in a simple reconfigurable phototransistor structure is presented. This standalone device exhibits inherent characteristics that enable neuromorphic image pre-processing and recognition. Fundamentally, the unique photoresponse induced by oxidation-related defects in 2D black phosphorus (BP) is exploited to achieve visual memory, wavelength-selective multibit programming, and erasing functions, which allow in-pixel image pre-processing. Furthermore, all-optically driven neuromorphic computation is demonstrated by machine learning to classify numbers and recognize images with an accuracy of over 90%. The devices provide a promising approach toward neurorobotics, human-machine interaction technologies, and scalable bionic systems with visual data storage/buffering and processing
Description:Date Completed 12.03.2021
Date Revised 12.03.2021
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202004207