Nonlinear self calibrated spectrometer with single GeSe-InSe heterojunction device

Computational spectrometry is an emerging field that employs photodetection in conjunction with numerical algorithms for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, such photodetectors are typically nonlinear devices, which adds complexity to extracting optical spectra from their response. Here, we train an artificial neural network (ANN) to recover the full nonlinear spectral photoresponse of a single GeSe-InSe p-n heterojunction device. The device has a spectral range of 400-1100 nm, a small footprint of ~25×25 〖μm〗^2, and a mean reconstruction error of 〖2×10〗^(-4) for the power spectrum at 0.35 nm. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging.

Methods
Photoresponse Characterization: All measurements of photocurrent as a function of bias voltage were performed at room temperature ( 25  ± 1 °C) under vacuum conditions at ~10-5 Torr. The photocurrent was measured with incident light modulated by a mechanical chopper at frequency of 1 kHz, and with low noise current pre-amplifier (Femto DLPCA-200) and lock-in amplifier (Model SR830). In this photocurrent measurement, the heterojunction was illuminated by seven light-emitting diodes and a Laser Driven Light Source (LDLS) as a white-light source combined with a set of bandpass filters and with transparency printed filters (see supplementary information for details). The reference spectrum of each light source was measured with a Thermo Fisher Scientific Nicolet-iS50R Fourier Transform Infrared (FTIR) spectrometer connected to an external silicon detector (Thorlabs FDS100) and the spectra were normalized to the silicon detector’s calibrated responsivity.

Computational Spectrum Reconstruction: The dataset (see Supplementary materials) comprising (i) 10 power spectra acquired from seven LED light sources; (ii) Laser-driven white light source (LDLS) with filter sets: (a) a bandpass filter set (at width of 25 nm) in the spectral range of 400-1100 nm; (b) a bandpass filter set (at width of 40 nm) in the spectral range of 400-1100 nm; (c) a set of homemade filters produced by printing transparencies. All filter sets were spectrally characterized. Overall, 50 filters were used with the white-light source. The data were split randomly into training (80%) and testing (20%) sets. The reconstruction code utilized TensorFlow, sklearn, numpy and scipy software packages.