A Full‐Quantum‐Dot Optoelectronic Memristor for In‐Sensor Reservoir Computing System with Integrated Functions

Abstract In‐sensor computing hardware with signal sensing and dynamic signal processing capabilities—inspired by the human sensory system—have attracted interest as the rapid proliferation of data sensing and computation in the era of big data. Here, an in‐sensor reservoir computing (RC) system with integrated functions of signal sensing, preprocessing, and RC in a single device is developed, by constructing a full quantum dot optoelectronic memristor (FQDOM) based on a ZnO QDs/CdSe QDs/ZnO QDs heterojunction. The device shows nonlinear and short‐term memory behavior in response to both electrical stimuli and optical signals covering the spectra from ultraviolet to red light. The FQDOM can achieve color perception. The in‐sensor RC system can sense varied types of input signal, exhibiting multisensory fusion capability by fashion‐MNIST classification. More importantly, these proposed FQDOMs can preprocess signal and then send for RC in sensor, which effectively reduces the bit error rate of RC systems. The processing capability of the in‐sensor RC system for temporal signals is further demonstrated by a dynamic gesture perception recognition task, showing an accuracy of up to 92.59%. This research provides an effective way to design advanced in‐sensor computational systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-02-26
DOI
https://doi.org/10.1002/adfm.202423548
Citations
17
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
42.18

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article

A Full‐Quantum‐Dot Optoelectronic Memristor for In‐Sensor Reservoir Computing System with Integrated Functions

Shitong Peng, Yi Zhao, Deli Li, Fushan Li et al.
17 citations
Advanced Functional Materials
Neural Networks and Reservoir Computing
42.18
article

A Full‐Quantum‐Dot Optoelectronic Memristor for In‐Sensor Reservoir Computing System with Integrated Functions

Shitong Peng, Yi Zhao, Deli Li, Fushan Li, Liangxu Lin, Jiayu Sun, Yue Wang, Yang Liu, Yangbin Zhu, Xuan Guo, Qinghong Lin, Kejia You, Zhen Wang
article en
17 citations

Abstract

Abstract In‐sensor computing hardware with signal sensing and dynamic signal processing capabilities—inspired by the human sensory system—have attracted interest as the rapid proliferation of data sensing and computation in the era of big data. Here, an in‐sensor reservoir computing (RC) system with integrated functions of signal sensing, preprocessing, and RC in a single device is developed, by constructing a full quantum dot optoelectronic memristor (FQDOM) based on a ZnO QDs/CdSe QDs/ZnO QDs heterojunction. The device shows nonlinear and short‐term memory behavior in response to both electrical stimuli and optical signals covering the spectra from ultraviolet to red light. The FQDOM can achieve color perception. The in‐sensor RC system can sense varied types of input signal, exhibiting multisensory fusion capability by fashion‐MNIST classification. More importantly, these proposed FQDOMs can preprocess signal and then send for RC in sensor, which effectively reduces the bit error rate of RC systems. The processing capability of the in‐sensor RC system for temporal signals is further demonstrated by a dynamic gesture perception recognition task, showing an accuracy of up to 92.59%. This research provides an effective way to design advanced in‐sensor computational systems.

Advanced Functional MaterialsVol. 35(30)
Fujian Normal University (CN), Wenzhou University (CN), Wenzhou University of Technology, Fuzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Fujian Normal University
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
42.18
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