Clock‐Free Optical Communication Based on Interface Defect Control Bimodal Neuromorphic Devices

Abstract Neuromorphic devices based on carrier relaxation have demonstrated significant potential in simplifying the integration of sensory‐computing neural network chips. However, typical relaxation designs rely on defect sustained‐release mechanisms and interaction with bulk channel carriers, which compromise sensing performance and hinder the effective coordination of gains between computing and sensing functions. Herein, the limitation is addressed by confining the interaction between defects and carriers to a 2D interface involving Tellurium (Te) and the Hafnium Dioxide (HfO 2 )/hexagonal Boron Nitride (h‐BN). This approach enables high‐fidelity perception and computation of optical communication timing sequences through precise defect modulation to allocate modal weights. The device exhibits competitive photo‐electric conversion performance for communication digital signals under zero gate bias. More notably, under applied gate bias, the device can function as an optical synapse with a rise time τ rise of 1.04 ms, the synaptic response processes can be directly utilized as a sequence detector rather than merely contributing to the final conductance. By simultaneously performing sensing and computing functionalities within a single device, high‐precision timing recovery is achieved for optical communication using a spiking neural network (SNN) algorithm. This work presents a pioneering verification and offers a potential high‐performance architecture reference for compact clock‐free optical communication modules.

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Publication Details

Journal
Advanced Electronic Materials
Published
2025-11-11
DOI
https://doi.org/10.1002/aelm.202500580
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Clock‐Free Optical Communication Based on Interface Defect Control Bimodal Neuromorphic Devices

Ruolan Wen, Changjian Zhou, Jiwei Chen, Yihong Sun et al.
Advanced Electronic Materials
Advanced Memory and Neural Computing
article

Clock‐Free Optical Communication Based on Interface Defect Control Bimodal Neuromorphic Devices

Ruolan Wen, Changjian Zhou, Jiwei Chen, Yihong Sun, Yingjie Luo, Aumber Abbas, Yueyi Sun, Mengqi Che
article en

Abstract

Abstract Neuromorphic devices based on carrier relaxation have demonstrated significant potential in simplifying the integration of sensory‐computing neural network chips. However, typical relaxation designs rely on defect sustained‐release mechanisms and interaction with bulk channel carriers, which compromise sensing performance and hinder the effective coordination of gains between computing and sensing functions. Herein, the limitation is addressed by confining the interaction between defects and carriers to a 2D interface involving Tellurium (Te) and the Hafnium Dioxide (HfO 2 )/hexagonal Boron Nitride (h‐BN). This approach enables high‐fidelity perception and computation of optical communication timing sequences through precise defect modulation to allocate modal weights. The device exhibits competitive photo‐electric conversion performance for communication digital signals under zero gate bias. More notably, under applied gate bias, the device can function as an optical synapse with a rise time τ rise of 1.04 ms, the synaptic response processes can be directly utilized as a sequence detector rather than merely contributing to the final conductance. By simultaneously performing sensing and computing functionalities within a single device, high‐precision timing recovery is achieved for optical communication using a spiking neural network (SNN) algorithm. This work presents a pioneering verification and offers a potential high‐performance architecture reference for compact clock‐free optical communication modules.

Advanced Electronic MaterialsVol. 12(1)
Nanyang Technological University (SG), Jiangsu University of Technology (CN), South China University of Technology (CN)
National Natural Science Foundation of China, South China University of Technology
Openalex Percentile: Top 67%
Advanced Memory and Neural Computing
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