Ultrafast non-volatile charge storage and mid-infrared photoluminescence in LuminoMem tellurium devices for in-memory computing

Integrated optoelectronic systems strive to combine the logic and memory density of electronics with the bandwidth of photonics, but monolithic realization is impeded by the inefficient electronic-to-photonic interface. Current architectures rely on separate readout circuitry and modulators, creating bottlenecks in energy and latency, while existing direct transduction methods often compromise on switching speed or non-volatility. Here, we develop an ultrafast, non-volatile optoelectronic memory, named LuminoMem, that integrates electrical storage and mid-infrared light emission ( ~ 3.4 μm) in a single device. The device utilizes a floating-gate architecture, in which the semiconductor tellurium serves simultaneously as a charge-storage layer and an emissive medium. This design enables nanosecond-scale electrical programming of non-volatile photoluminescence, allowing direct optical access to stored states without external modulation. We demonstrate that LuminoMem achieves 4-bit optical storage capacity and enables highly accurate image-recognition neural network simulations, providing a hardware foundation that co-integrates optical emission, memory, and computing capabilities. The monolithic integration of memory and photonic devices is normally limited by inefficient electronic-to-photonic interfaces. Here, the authors report optoelectronic floating-gate memory devices based on tellurium, integrating multi-bit electrical storage and mid-infrared light emission at 3.4 μm with nanosecond-scale electrical programming speed.

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Journal
Nature Communications
Published
2026-07-16
DOI
https://doi.org/10.1038/s41467-026-75638-w
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Ultrafast non-volatile charge storage and mid-infrared photoluminescence in LuminoMem tellurium devices for in-memory computing

Mingyang Qin, Changgan Zeng, Dehong Yang, Huawei Liu et al.
Nature Communications
Neural Networks and Reservoir Computing
article

Ultrafast non-volatile charge storage and mid-infrared photoluminescence in LuminoMem tellurium devices for in-memory computing

Mingyang Qin, Changgan Zeng, Dehong Yang, Huawei Liu, Dong Sun, B H Cheng, Anlian Pan, Jing Liu, Dong Li, Lin Li, Yan Wang, Delang Liang, Shiyu Wang, Yuchun Chen, Jie Sheng
article en

Abstract

Integrated optoelectronic systems strive to combine the logic and memory density of electronics with the bandwidth of photonics, but monolithic realization is impeded by the inefficient electronic-to-photonic interface. Current architectures rely on separate readout circuitry and modulators, creating bottlenecks in energy and latency, while existing direct transduction methods often compromise on switching speed or non-volatility. Here, we develop an ultrafast, non-volatile optoelectronic memory, named LuminoMem, that integrates electrical storage and mid-infrared light emission ( ~ 3.4 μm) in a single device. The device utilizes a floating-gate architecture, in which the semiconductor tellurium serves simultaneously as a charge-storage layer and an emissive medium. This design enables nanosecond-scale electrical programming of non-volatile photoluminescence, allowing direct optical access to stored states without external modulation. We demonstrate that LuminoMem achieves 4-bit optical storage capacity and enables highly accurate image-recognition neural network simulations, providing a hardware foundation that co-integrates optical emission, memory, and computing capabilities. The monolithic integration of memory and photonic devices is normally limited by inefficient electronic-to-photonic interfaces. Here, the authors report optoelectronic floating-gate memory devices based on tellurium, integrating multi-bit electrical storage and mid-infrared light emission at 3.4 μm with nanosecond-scale electrical programming speed.

Nature Communications
University of Science and Technology of China (CN), Zhongyuan University of Technology (CN), Tianjin University (CN), Hunan University (CN), Hunan Normal University (CN), Peking University (CN), Zhengzhou University of Light Industry (CN), Changsha Normal University (CN), Zhengzhou University (CN), Collaborative Innovation Center of Quantum Matter (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 6%
Neural Networks and Reservoir Computing
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