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 dist..., 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 Reconstructio..., , # Nonlinear Self-Calibrated Spectrometer with Single GeSe-InSe Heterojunction Device [https://doi.org/10.5061/dryad.d7wm37q7t](https://doi.org/10.5061/dryad.d7wm37q7t) **This datasheet comprises a total of 32 files, encompassing figures 2 to 5 in the manuscript and Figures S2 to S8 in the Supplementary Materials. The naming convention of the datasheet aligns with the respective figure numbers in both the manuscript and Supplementary Materials.** ## Description of the data and file structure ANN = artificial neural network **Datasheet Fig 2B, S2**, and **S3** correspond to the Raman spectra. **Datasheet Fig 2C** and **S4** correspond to the InSe/GeSe current-voltage output curve while **datasheet S4** shows an enlarged view of the device current-voltage output curve, measured over the range of ±1V. **Datasheet Fig. 3 A-H**, the training set, and its nonlinear fitting. (Datasheet 3A and 3B) The spectral power density of LED sources from the nonlinear training set, **(Datasheet 3C, ...计算光谱学（Computational spectrometry）是一门新兴交叉领域，其结合光电探测与数值算法开展光谱测量工作。由层状材料制备的紧凑型单通道光电探测器尤为引人关注：这类器件可摒弃传统光谱仪中庞大的机械与光学组件，且可通过异质结构工程化设计，灵活优化器件性能。然而，此类光电探测器通常为非线性器件，这为从其响应信号中提取光学光谱增添了复杂度。本文中，我们训练了人工神经网络（Artificial Neural Network, ANN），以复原单个GeSe-InSe p-n异质结器件的全非线性光谱光电响应。该器件的光谱响应范围为400~1100 nm，器件占地面积仅约25×25 μm²，在0.35 nm分辨率下的功率光谱平均重构误差为2×10^-4。基于本器件，我们展示了一种应对同色异谱（metamerism）问题的解决方案——即不同功率光谱分布的颜色呈现表观颜色匹配的现象…… **光电响应表征**：所有关于光电流随偏置电压变化的测量均在室温（25±1 ℃）、真空环境（约10^-5 Torr）下开展。测量过程中，入射光通过频率为1 kHz的机械斩波器进行调制，并采用低噪声电流前置放大器（Femto DLPCA-200）与锁相放大器（SR830型号）完成光电流采集。本次光电流测量中，异质结器件的照明光源包括7个发光二极管与1台激光驱动白光光源（Laser Driven Light Source, LDLS），并搭配了一系列带通滤波器与透明印刷滤光片（详细参数参见补充材料）。每种光源的参考光谱均通过赛默飞世尔科技 Nicolet-iS50R 傅里叶变换红外（Fourier Transform Infrared, FTIR）光谱仪采集，该光谱仪外接硅探测器（Thorlabs FDS100），最终所有光谱均基于该硅探测器的标定响应度完成归一化。 **计算光谱重构……** # **基于单GeSe-InSe异质结器件的非线性自校准光谱仪** [https://doi.org/10.5061/dryad.d7wm37q7t](https://doi.org/10.5061/dryad.d7wm37q7t) 本数据集共包含32个文件，涵盖论文中的图2至图5以及补充材料中的图S2至图S8。数据集的文件命名规则与论文及补充材料中的对应图号保持一致。 ## **数据与文件结构说明** 注：ANN即人工神经网络（Artificial Neural Network） **数据集图2B、补充图S2与S3**对应拉曼光谱。 **数据集图2C与补充图S4**对应InSe/GeSe异质结的电流-电压输出特性曲线，其中补充图S4展示了±1V偏置范围内器件电流-电压输出曲线的放大视图。 **数据集图3A-H**：训练集及其非线性拟合结果。（数据集图3A与3B）非线性训练集内LED光源的光谱功率密度，**（数据集图3C……）**