Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing
Recently, the rising demand for data-based applications has driven the convergence of image sensing, memory, and computing unit interfaces. While specialized electronic hardware has spurred advancements in the in-memory and in-sensor computing, integrating the entire signal-processing chain into a single device still faces significant challenges. Here, a reconfigurable all-optical controlled memristor with the selector-free feature is demonstrated. The conductance of the device can be controlled within the pure light domain, which enables it to integrate sensing, memory, and computing together. The integrate-and-fire behavior is also realized through electrical stimuli. Furthermore, the device exhibits an excellent rectifying ratio and nonlinearity to overcome the sneak current. Finally, an in-memory sensing and computing architecture is realized through reservoir computing based on neuron and synaptic functions mimicked by the proposed device. Such an all-in-one paradigm facilitates the computing architecture with low energy consumption, low latency, and reduced hardware complexity.
Authors
- Hao Zhu (ORCID: https://orcid.org/0000-0003-3890-6871)
- Lu Chen (ORCID: https://orcid.org/0009-0008-1201-676X)
- David Wei Zhang
- Tianyu Wang (ORCID: https://orcid.org/0000-0002-6087-6376)
- Qingqing Sun (ORCID: https://orcid.org/0000-0002-6533-1834)
- Lin Chen (ORCID: https://orcid.org/0000-0002-7145-7564)
- Jialin Meng (ORCID: https://orcid.org/0000-0002-2166-3220)
- Jieru Song (ORCID: https://orcid.org/0009-0007-1034-6645)
- Kangli Xu (ORCID: https://orcid.org/0000-0002-8077-3593)
Institutions
- Shandong University (CN)
- Fudan University (CN)
- Shanghai Innovative Research Center of Traditional Chinese Medicine (CN)
- Shanghai Fudan Microelectronics (China) (CN)
Publication Details
- Journal
- ACS Nano
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1021/acsnano.4c09199
- Citations
- 22
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.59
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Science and Technology Commission of Shanghai Municipality
- Natural Science Foundation of Shandong Province
- National Key Research and Development Program of China