Photoelectric Memristor With Self-Powered for Dynamic Information Recognition

The bionic vision system realizes the functions of optical perception of external information and image information processing. The photoelectric synaptic device is the basic unit of this system. In this work, a HfO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>2</roman></sub>/Bi<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>4</roman></sub>Ti<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>3</roman></sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>12</roman></sub> (BIT) heterojunction photoelectric memristor with synaptic behavior is introduced. The device generates a persistent photocurrent (PPC) through the capture and release of photogenerated carriers in the heterojunction. Various synaptic behaviors can be simulated under the stimulation of light pulses, including paired-pulse facilitation (PPF), spike-number-dependent plasticity, and spike-width-dependent plasticity. It can also realize the transformation from short-term memory (STM) to long-term memory (LTM). A 3 × 5 array is constructed and the time dynamic characteristics of the device are used to distinguish the dynamic input. The physical reservoir built by photoelectric synaptic devices can not only improve the speed of data processing but also achieve efficient data storage, which brings new possibilities for AI in the fields of image recognition, speech recognition, and natural language processing.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Electron Devices
Published
2025-05-06
DOI
https://doi.org/10.1109/ted.2025.3565192
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Photoelectric Memristor With Self-Powered for Dynamic Information Recognition

Qijun Sun, Dong-Liang Li, Xin‐Gui Tang, Yanping Jiang et al.
1 citations
IEEE Transactions on Electron Devices
Advanced Memory and Neural Computing
0.73
article

Photoelectric Memristor With Self-Powered for Dynamic Information Recognition

Qijun Sun, Dong-Liang Li, Xin‐Gui Tang, Yanping Jiang, Zhenhua Tang, Wenhua Li, Dan Zhang, Zhilong Chen
article en
1 citations

Abstract

The bionic vision system realizes the functions of optical perception of external information and image information processing. The photoelectric synaptic device is the basic unit of this system. In this work, a HfO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>2</roman></sub>/Bi<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>4</roman></sub>Ti<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>3</roman></sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>12</roman></sub> (BIT) heterojunction photoelectric memristor with synaptic behavior is introduced. The device generates a persistent photocurrent (PPC) through the capture and release of photogenerated carriers in the heterojunction. Various synaptic behaviors can be simulated under the stimulation of light pulses, including paired-pulse facilitation (PPF), spike-number-dependent plasticity, and spike-width-dependent plasticity. It can also realize the transformation from short-term memory (STM) to long-term memory (LTM). A 3 × 5 array is constructed and the time dynamic characteristics of the device are used to distinguish the dynamic input. The physical reservoir built by photoelectric synaptic devices can not only improve the speed of data processing but also achieve efficient data storage, which brings new possibilities for AI in the fields of image recognition, speech recognition, and natural language processing.

IEEE Transactions on Electron DevicesVol. 72(6)
Guangdong University of Technology (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 29%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.