Oscillatory Neural Network with Tunable Frequency for Brain-Inspired Neuromorphic Computing
We present a silicon transistor-based oscillator with frequency tunability (SOFT) for brain-inspired neuromorphic computing. It consists of a single transistor-based oscillator (1T-O) and a single transistor-based resistor (1T-R), which is a nonvolatile memory device that provides conductance modulation to enable frequency tunability. Because the 1T-O and 1T-R are homologous metal-oxide-semiconductor field-effect transistors (MOSFETs), they are structurally identical but function differently. We simultaneously integrated them on the same wafer using complementary metal-oxide-semiconductor (CMOS) fabrication due to their homogeneity. We then demonstrate template matching by resistively coupling two SOFTs and classify temporal signals using first-harmonic injection locking (FHIL) with four SOFTs, leveraging their frequency tunability. This SOFT is well-suited for high-density oscillatory neural networks (ONNs) with low-cost CMOS fabrication, offering significant potential for energy-efficient and areal scalability.
Authors
- Seong‐Yun Yun (ORCID: https://orcid.org/0000-0003-4988-1120)
- Joon‐Kyu Han (ORCID: https://orcid.org/0000-0002-8736-9091)
- Yang‐Kyu Choi (ORCID: https://orcid.org/0000-0001-5480-7027)
- Y. C. Chung
Institutions
- Sogang University (KR)
- Korea Advanced Institute of Science and Technology (KR)
Publication Details
- Journal
- Nano Letters
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1021/acs.nanolett.5c00376
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Research Foundation of Korea