AI 中文总结
本研究提出紧凑变分神经网络，利用单个二维钙钛矿光电探测器的非线性动力学编码光场，通过机器学习实现光谱重建，在少于400次实验数据训练下，波长重建R²达0.958，为紧凑光谱系统提供新方案。
AI 中文摘要
光谱学传统上在探测前会分离光频率，这对设备的 footprint（ footprint 指设备尺寸）、复杂度和可扩展性构成持续限制。本文建立了一种替代范式：单个二维钙钛矿光电探测器的非线性光电动力学对入射光场进行物理编码，机器学习则执行逆光谱重建。我们采用平面氟化苯乙胺铅碘（F-PEAI）光电探测器，利用光载流子产生、俘获、界面输运及场依赖载流子动力学耦合效应产生的波长和辐照度依赖电流-电压特征。紧凑变分编码器-解码器通过将正向和反向电压扫描独立投影到截断勒让德多项式基上，再通过概率潜在表示映射到连续光谱参数，保留这些响应的功能和依赖历史的结构。该模型在少于400次实验电压扫描上训练，可推广到训练中未包含的激发波长，以R²=0.958重建波长，平均绝对误差为8.1 nm，同时以R²=0.987恢复对数归一化辐照度。电压分辨分析进一步揭示，波长和辐照度在非线性器件响应中的编码方式不同，不同偏置区域携带互补光学信息。这些结果确立了非线性材料和界面动力学作为光谱学的计算资源，并指向硬件-算法协同设计，即共同设计材料、界面和推理架构以最大化信息含量，从而实现无需色散光学或探测器阵列的紧凑光谱系统。
英文摘要
Spectroscopy conventionally separates optical frequencies before detection, imposing persistent constraints on footprint, complexity and scalability. Here we establish an alternative paradigm in which the nonlinear optoelectronic dynamics of a single two-dimensional perovskite photodetector physically encode the incident optical field and machine learning performs the inverse spectral reconstruction. Using a planar fluorinated phenethylammonium lead iodide (F-PEAI) photodetector, we exploit wavelength- and irradiance-dependent current-voltage signatures arising from the coupled effects of photocarrier generation, trapping, interfacial transport and field-dependent carrier dynamics. A compact variational encoder-decoder preserves the functional and history-dependent structure of these responses by independently projecting forward and reverse voltage sweeps onto a truncated Legendre-polynomial basis before mapping them through a probabilistic latent representation to continuous spectral parameters. Trained on fewer than 400 experimental voltage sweeps, the model generalises to excitation wavelengths excluded from training, reconstructing wavelength with $R^2=0.958$ and a mean absolute error of 8.1 nm, while recovering log-normalised irradiance with $R^2=0.987$. Voltage-resolved analysis further reveals that wavelength and irradiance are encoded differently across the nonlinear device response, with distinct bias regions carrying complementary optical information. These results establish nonlinear material and interface dynamics as a computational resource for spectroscopy and point towards hardware-algorithm co-design in which materials, interfaces and inference architectures are engineered jointly to maximise information content, enabling compact spectroscopic systems without dispersive optics or detector arrays.
Comments28 pages in main article (29 pages in the supporting information), 5 figures in the main article (11 figures in the supplementary material)
