In-Sensor Spectral Machine Learning with Electrically Tunable Bipolar Photodetectors
We report spectral kernel machines leveraging electrically tunable photodetectors for in-sensor machine-learning analysis of spectral data at low power and high speed. This innovation opens opportunities for diverse applications, from chemometrics to semiconductor metrology.
Authors
- Dehui Zhang (ORCID: https://orcid.org/0000-0002-7077-0639)
- Aydogan Özcan (ORCID: https://orcid.org/0000-0002-0717-683X)
- Ali Javey (ORCID: https://orcid.org/0000-0001-7214-7931)
- Hyong Min Kim (ORCID: https://orcid.org/0000-0002-0094-4189)
- Jamie Geng (ORCID: https://orcid.org/0009-0003-8707-6533)
- Yuhang Li (ORCID: https://orcid.org/0000-0002-9364-4125)
- Marco Ma (ORCID: https://orcid.org/0009-0004-7584-0917)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_si.2025.ss118_4
- Citations
- 1
- Primary Topic
- Advanced Optical Sensing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.19