Low‐Power Dual‐Functional Neuromorphic Optoelectronic Device Based on Bi <sub>4.15</sub> Nd <sub>0.85</sub> Ti <sub>3</sub> FeO <sub>15</sub> /ZnO Heterojunction

Constructing multifunctional neuromorphic optoelectronic devices that integrate sensing, memory and computing is an important approach to overcome the computational bottlenecks of the traditional von Neumann architecture and improve the performance of artificial visual systems. In this work, a Bi<sub>4</sub>.<sub>15</sub>Nd<sub>0</sub>.<sub>85</sub>Ti<sub>3</sub>FeO<sub>15</sub>(BNTF)/ZnO ferroelectric-semiconductor heterojunction device was designed and fabricated via the sol-gel method, realizing the integration of self-powered photodetection and low-power optical synapse in a single two-terminal device. Under zero bias, the device exhibited polarization-tunable photoresponsivity (R<sub>up</sub>/R<sub>down</sub> = 4.1) with high response speed, and thus excellent imaging capability and photoelectric coupling logic operation functions. Under small bias voltage (≤0.5 V), it could act as an artificial optical synapse device and achieve multiple types of synaptic plasticity. Notably, by the self-denoising function of the optical synapse, the recognition accuracy of digit images improved from 87.5% to 96.8%. The in-sensor reservoir computing system based on BNTF/ZnO synapse array achieved a high accuracy of 97.3% in moving target recognition. The device also featured an ultralow power consumption of 4.6 fJ, matching or even lower than that of most biological synapses. This study revealed the unique advantages and application prospects of ferroelectric-semiconductor heterojunction devices in constructing low-power integrated imaging-memory-computing vision systems.

Authors

Institutions

Publication Details

Journal
Small
Published
2026-01-23
DOI
https://doi.org/10.1002/smll.202513679
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Low‐Power Dual‐Functional Neuromorphic Optoelectronic Device Based on Bi 4.15 Nd 0.85 Ti 3 FeO 15 /ZnO Heterojunction

Q. M., Shuhao Jiang, Biwei Shen, Zeyu Xiao et al.
Small
Advanced Memory and Neural Computing
article

Low‐Power Dual‐Functional Neuromorphic Optoelectronic Device Based on Bi 4.15 Nd 0.85 Ti 3 FeO 15 /ZnO Heterojunction

Q. M., Shuhao Jiang, Biwei Shen, Zeyu Xiao, Jiajun Zhu, J. Lian, Fengzhen Huang, K. Cao, Yuanyuan Zhang, Xueli Hu
article en

Abstract

Constructing multifunctional neuromorphic optoelectronic devices that integrate sensing, memory and computing is an important approach to overcome the computational bottlenecks of the traditional von Neumann architecture and improve the performance of artificial visual systems. In this work, a Bi<sub>4</sub>.<sub>15</sub>Nd<sub>0</sub>.<sub>85</sub>Ti<sub>3</sub>FeO<sub>15</sub>(BNTF)/ZnO ferroelectric-semiconductor heterojunction device was designed and fabricated via the sol-gel method, realizing the integration of self-powered photodetection and low-power optical synapse in a single two-terminal device. Under zero bias, the device exhibited polarization-tunable photoresponsivity (R<sub>up</sub>/R<sub>down</sub> = 4.1) with high response speed, and thus excellent imaging capability and photoelectric coupling logic operation functions. Under small bias voltage (≤0.5 V), it could act as an artificial optical synapse device and achieve multiple types of synaptic plasticity. Notably, by the self-denoising function of the optical synapse, the recognition accuracy of digit images improved from 87.5% to 96.8%. The in-sensor reservoir computing system based on BNTF/ZnO synapse array achieved a high accuracy of 97.3% in moving target recognition. The device also featured an ultralow power consumption of 4.6 fJ, matching or even lower than that of most biological synapses. This study revealed the unique advantages and application prospects of ferroelectric-semiconductor heterojunction devices in constructing low-power integrated imaging-memory-computing vision systems.

SmallVol. 22(17)
Collaborative Innovation Center of Advanced Microstructures (CN)
Affordable and clean energy
Openalex Percentile: Top 80%
Advanced Memory and Neural Computing
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.