A Photosensitive Spiking Neuron Using a Single Band-Modulation Device With Tunable Spiking Sensitivity
Bio-inspired visual systems require a critical component to perceive optical stimuli and convert them into spiking signals. In this study, a compact photosensitive spiking neuron is demonstrated using a single band-modulation device called zero subthreshold swing and zero impact ionization FET (Z<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-FET). This device is fully compatible with the standard CMOS process and features integrate-and-fire (IF) neural behavior. The firing threshold and spiking frequency of the Z<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-FET neuron can be modulated by incident light with various intensities and wavelengths, similar to the photoresponse observed in biological neurons. TCAD simulations validate the working principle, originating from the deep depletion effect and photo-induced reduction of energy barriers. Inspired by biological neurons with adjustable spike encoding functionality, the Z<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-FET neuron achieves controllable photoresponsive frequency when the input current, top-gate, and back-gate voltages are tuned. With its excellent scaling capability, low energy consumption, and tunable spiking sensitivity, the photosensitive Z<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-FET neuron holds great potential in multifunctional neuromorphic applications, such as intelligent visual perception and neural computing.
Authors
- S. Cristoloveanu (ORCID: https://orcid.org/0000-0002-3576-5586)
- Chenyu Liu (ORCID: https://orcid.org/0009-0003-0306-0957)
- Yong Xu (ORCID: https://orcid.org/0000-0002-6800-234X)
- Fanyu Liu (ORCID: https://orcid.org/0000-0002-6154-4971)
- Peng Zhou (ORCID: https://orcid.org/0000-0002-7301-1013)
- Yingxin Chen (ORCID: https://orcid.org/0000-0001-9372-8479)
- Tian Tian (ORCID: https://orcid.org/0009-0000-3950-6154)
- Hui Xie (ORCID: https://orcid.org/0009-0005-7444-184X)
- Jing Wan (ORCID: https://orcid.org/0000-0002-6339-4006)
Institutions
- Chinese Academy of Sciences (CN)
- Fudan University (CN)
- Institute of Microelectronics (CN)
- Shanghai Fudan Microelectronics (China) (CN)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System
- State Key Laboratory of ASIC and System
Publication Details
- Journal
- IEEE Transactions on Electron Devices
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1109/ted.2024.3452073
- Citations
- 1
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Guangxi Key Research and Development Program