In-Context Learning for Sim2Real Reconstructive Spectroscopy: Bridging Modern Machine Lear — NSF Award to Regents of the Universit
Optical spectroscopy plays a crucial role across various scientific fields, from chemical process analysis to material identification and fluorescence detection. Driven by the demand for portable and field-deployable tools, miniaturizing spectroscopic systems onto chip-scale platforms has become a major research focus.
| Award title | In-Context Learning for Sim2Real Reconstructive Spectroscopy: Bridging Modern Machine Lear |
|---|---|
| Award ID | 2532643 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $644,407 |
| Principal investigator | Qing Qu |
| Program | CCSS-Comms Circuits & Sens Sys |
| Start date | 10/15/2025 |
| Abstract | Optical spectroscopy plays a crucial role across various scientific fields, from chemical process analysis to material identification and fluorescence detection. Driven by the demand for portable and field-deployable tools, miniaturizing spectroscopic systems onto chip-scale platforms has become a major research focus. This project leverages cutting-edge machine learning techniques for spectral reconstruction to develop a compact, on-chip spectrometer supporting ultraviolet-visible fluorescence, |
| Source | NSF Awards |
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