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Unique Hyperspectral Response Design Enabled by Periodic Surface Textures in Photodiodes
Ahasan Ahamed1, Amita Rawat1, Lisa N McPhillips1
1Electrical and Computer Engineering, University of California-Davis, Davis, California 95616, United States.
Summary
Photon-trapping surface textures (PTSTs) enable miniaturized hyperspectral imaging systems by eliminating external optics. This innovation offers high-speed, high-gain photodiodes for cost-effective spectral analysis.
Area of Science:
- Photonics
- Materials Science
- Spectroscopy
Background:
- Hyperspectral imaging applications are limited by bulky, complex optical components.
- Existing spectral response devices require intricate filters and dispersion lenses.
Purpose of the Study:
- To propose a novel method for designing engineered spectral responses using photon-trapping surface textures (PTSTs).
- To develop and validate an analytical model for electromagnetic wave coupling with PTSTs.
- To demonstrate the feasibility of PTST-equipped photodiodes for miniaturized hyperspectral imaging.
Main Methods:
- Developed an analytical model for electromagnetic wave coupling using the effective refractive index of silicon with PTSTs.
- Validated the model against simulations and experimental data from PTST-equipped photodiodes fabricated using CMOS-compatible processes.
- Characterized the electrical and optical performance of the fabricated photodiodes.
Main Results:
- Observed a linear relationship between peak coupling wavelength and PTST period, and a proportional relation to PTST diameters.
- Identified a significant correlation between inter-PTST spacing and wave propagation modes.
- Demonstrated high-speed (27 ps), high-gain (M: 90), low-voltage (breakdown voltage: ~8.0 V) performance of PTST photodiodes.
Conclusions:
- PTSTs eliminate the need for external diffraction optics, enabling system miniaturization for hyperspectral imaging.
- The developed model accurately predicts spectral response, facilitating on-chip spectrometer integration.
- These advancements pave the way for cost-effective, real-time hyperspectral imaging systems.

