Miniaturized spectral sensing with a tunable optoelectronic interface
Creators
- 1. Aalto University
- 2. Shanghai Jiao Tong University
- 3. University of Eastern Finland
- 4. Gwangju Institute of Science and Technology
- 5. VTT Technical Research Centre of Finland
- 6. Zhejiang University
- 7. University of Cambridge
Description
Reconstructive optoelectronic spectroscopy has generated significant interest in the miniaturization of traditional spectroscopic tools, such as spectrometers. However, most state-of-the-art demonstrations face fundamental limits of rank-deficiency in the photoresponse matrix. In this work, we demonstrate a miniaturized spectral sensing system using an electrically tunable compact optoelectronic interface, which generates distinguishable photoresponses from various input spectra, enabling accurate spectral identification with a device footprint of 5μm×5μm. We report narrow-band spectral sensing with peak accuracies of ∼0.19 nm in free space and ∼2.45 nm on-chip. Additionally, we implement broadband complex spectral sensing for material identification, applicable to organic dyes, metals, semiconductors, and dielectrics. This work advances high-performance, miniaturized optical spectroscopy for both free-space and on-chip applications, offering cost-effective solutions, broad applicability, and scalable manufacturing.
Notes
Funding provided by: European Research Council
Crossref Funder Registry ID:
Award Number: 834742
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 314810
Funding provided by: Academy of Finland Flagship Programme
Crossref Funder Registry ID:
Award Number: 320167
Funding provided by: EU H2020-MSCA-RISE-872049
Crossref Funder Registry ID:
Award Number: IPN-Bio
Funding provided by: Jane and Aatos Erkko foundation and the Technology Industries of Finland centennial foundation
Crossref Funder Registry ID:
Award Number: Future Makers 2022
Funding provided by: Academy of Finland Flagship Programme
Crossref Funder Registry ID:
Award Number: 320166
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 336144
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 352780
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 352930
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 353364
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 333982
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 340932
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 348920
Funding provided by: Academy of Finland
Crossref Funder Registry ID:
Award Number: 336818
Funding provided by: European Union's Horizon 2020 Research and Innovation Program
Crossref Funder Registry ID:
Award Number: 820423
Funding provided by: European Union's Horizon 2020 Research and Innovation Program
Crossref Funder Registry ID:
Award Number: 965124
Funding provided by: National Research Foundation of Korea
ROR ID: https://ror.org/013aysd81
Award Number: RS-2024-00463154
Funding provided by: Korean government Ministry of Trade, Industry, and Energy
Crossref Funder Registry ID:
Award Number: RS-2024-00431676
Methods
This is the very initial code version we used, this is a python version adapted from our earlier work, which was in Matlab (check https://doi.org/10.5281/zenodo.7012876). we believe python will provide more convenience in e.g., utilizing deep learning and AI based algorithm for further optimization.
Files
Files
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Additional details
Related works
- Is source of
- 10.5061/dryad.547d7wmh8 (DOI)