Spectrally Defined Bipolar Black Phosphorus Memristor Enables All‐Optical Boolean Logic and Multispectral Computing

S Shanwu Ke (Key Laboratory of Intelligent Sensing System and Security of the Ministry of Education, Hubei University 2 , Wuhan 430062,) Y Yang Li Y Yuanduo Qu (Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China) H Hao Huang M Mengdi Hao (Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China) L Li Yang Q Qiqian Wu (Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China) C Cong Ye P Paul K. Chu X Xue‐Feng Yu (Materials Artificial Intelligence Center, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Avenue Shenzhen 518055 P.R. China) J Jiahong Wang (School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China)

Abstract

ABSTRACT Although optoelectronic memristors with nonvolatile bipolar photoconductivity enable in‐sensor vision‐centric neuromorphic hardware, achieving wavelength‐defined polarity inversion across a broad spectrum remains a challenging task. Herein, a stable optoelectronic memristor composed of nonstoichiometric lead oxide (PbO x ) coated black phosphorus (BP) nanosheets is demonstrated. The optoelectronic processes in the PbO x ‐BP heterostructure result in programmable polar photoresponses across the 365 nm – 1,550 nm wavelength range. Visible light causes positive photoconductance via photoelectrochemical Ag + reduction and conductive filament reconstruction. Conversely, ultraviolet light drives the reverse photogenerated electron transfer to chemically oxidize the Ag CFs, while infrared light induces their localized melting via the photothermal effect. This bipolar optoelectronic tunability enables all‐optical Boolean logic operations, allowing for the realization of 14 binary functions through optical reconfiguration. Furthermore, multispectral computing tasks, including edge extraction and spectral noise suppression, are performed, yielding a classification accuracy of up to 98.6% for 16 crop species using an all‐optical convolutional neural network. The ultra‐thin oxide coating presents an effective surface modification approach to improve two‐dimensional devices, while the optoelectronic bipolarity establishes a framework for all‐optical modulation in neuromorphic machine vision.

Article Details

Volume / Issue Vol. 38, Issue 37
Published July 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (11)

S

Shanwu Ke

Key Laboratory of Intelligent Sensing System and Security of the Ministry of Education, Hubei University 2 , Wuhan 430062,

Y

Yang Li

Y

Yuanduo Qu

Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China

H

Hao Huang

M

Mengdi Hao

Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China

L

Li Yang

Q

Qiqian Wu

Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P. R. China

C

Cong Ye

P

Paul K. Chu

X

Xue‐Feng Yu

Materials Artificial Intelligence Center, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Avenue Shenzhen 518055 P.R. China

J

Jiahong Wang

School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China