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.
Authors
- Mingyang Qin (ORCID: https://orcid.org/0000-0001-5341-7465)
- Changgan Zeng (ORCID: https://orcid.org/0000-0001-8630-845X)
- Dehong Yang (ORCID: https://orcid.org/0000-0001-7896-8474)
- Huawei Liu (ORCID: https://orcid.org/0000-0003-4433-5075)
- Dong Sun (ORCID: https://orcid.org/0000-0002-0898-4548)
- B H Cheng
- Anlian Pan (ORCID: https://orcid.org/0000-0003-3335-3067)
- Jing Liu (ORCID: https://orcid.org/0000-0002-8993-4074)
- Dong Li (ORCID: https://orcid.org/0000-0003-0391-7060)
- Lin Li (ORCID: https://orcid.org/0000-0002-0533-7783)
- Yan Wang (ORCID: https://orcid.org/0000-0001-6076-0026)
- Delang Liang
- Shiyu Wang (ORCID: https://orcid.org/0009-0007-5888-4119)
- Yuchun Chen
- Jie Sheng
Institutions
- 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)
Publication Details
- 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
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China