Ultralow Power Artificial Synapses Based on CrPS<sub>4</sub>-Floating-Gate Transistor for the Emotional Simulation
With the rapid development of artificial intelligence and intelligent robot technology, integrating human emotions has become essential for improving interactions with machines. The amygdala, a key brain region for regulating emotions, plays an important role in advancing this technology by simulating internal emotional synaptic activities. However, it has been a significant challenge to perform simulation studies on synaptic activity within the amygdala during the process of emotional generation and transformation. Here, we propose a two-dimensional van der Waals floating gate synaptic transistor using CrPS4. Utilizing two weight dynamic updating characteristics of long-term potentiation and long-term depression, the synaptic activity related to emotional generation and transformation in the amygdala was effectively simulated. Due to the strong charge-trapping ability of CrPS4, the enhanced negative gate pulse increased channel conductance, thereby simulating various emotional levels. Meanwhile, the rate of decrease in channel conductance was regulated by the positive gate pulse voltage during long-term depression, allowing for the simulation of slow, rapid, and instantaneous emotional transitions. This work explored electronic devices that mimic the emotional biological function of the amygdala, displaying potential applications in human-computer interactions and intelligent robots.
Authors
- Zishuo Han (ORCID: https://orcid.org/0009-0003-2176-2063)
- Bin Fang (ORCID: https://orcid.org/0000-0002-6916-587X)
- Zhongyuan Liu (ORCID: https://orcid.org/0000-0002-5600-8998)
- Taiping Zeng (ORCID: https://orcid.org/0000-0002-8317-9469)
- Chuwei Kong (ORCID: https://orcid.org/0009-0002-4020-8917)
- Zhongming Zeng (ORCID: https://orcid.org/0000-0001-7240-2058)
- Zhipeng Yu (ORCID: https://orcid.org/0000-0001-9019-5494)
- Yuxuan Zeng (ORCID: https://orcid.org/0000-0003-4875-8112)
- Xuan Zhang (ORCID: https://orcid.org/0000-0003-0984-021X)
- Lin Geng (ORCID: https://orcid.org/0000-0001-6403-1672)
- Kun Ye (ORCID: https://orcid.org/0000-0003-4142-6596)
- Weiming Lv
- Lifeng Bian (ORCID: https://orcid.org/0000-0001-6196-7424)
Institutions
- Chinese Academy of Sciences (CN)
- Fudan University (CN)
- Yanshan University (CN)
- Suzhou Institute of Nano-tech and Nano-bionics (CN)
Publication Details
- Journal
- ACS Applied Nano Materials
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1021/acsanm.4c06195
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 3.65
Funders
- National Natural Science Foundation of China
- Suzhou Institute of Nanotechnology, Chinese Academy of Sciences