Optically driven intelligent computing with ZnO memristor.

All-optically controlling Artificial vision Logic-in-memory Memristor Nonvolatile neuromorphic computing ZnO thin film

Journal

Fundamental research
ISSN: 2667-3258
Titre abrégé: Fundam Res
Pays: China
ID NLM: 9918821688906676

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 15 04 2022
revised: 13 06 2022
accepted: 22 06 2022
medline: 25 7 2022
pubmed: 25 7 2022
entrez: 27 6 2024
Statut: epublish

Résumé

Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.

Identifiants

pubmed: 38933832
doi: 10.1016/j.fmre.2022.06.019
pii: S2667-3258(22)00296-5
pmc: PMC11197590
doi:

Types de publication

Journal Article

Langues

eng

Pagination

158-166

Informations de copyright

© 2022 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest in this work.

Auteurs

Jing Yang (J)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Lingxiang Hu (L)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China.

Liufeng Shen (L)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Jingrui Wang (J)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Peihong Cheng (P)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Huanming Lu (H)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Fei Zhuge (F)

Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China.
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China.
Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China.

Zhizhen Ye (Z)

Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China.
State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Classifications MeSH