Deciphering the composite information within a light field through a single photodetector, without optical and mechanical structures, is challenging. The difficulty lies in extracting multi-dimensional optical information from a single dimension of photocurrent. Emerging photodetectors based on information reconstruction have potential, yet they only extract information contained in the photoresponse current amplitude (responsivity matrix), neglecting the hidden information in response edges driven by carrier dynamics. Herein, by adjusting the thickness of the absorption layer and the interface electric field strength in the perovskite photodiode, we extend the transport and relaxation time of carriers excited by photons of different wavelengths, maximizing the spectrum richness of the edge waveform in the light-dark transition process. For the first time, without the need for extra optical and electrical components, the reconstruction of two-dimensional information of light intensity and wavelength has been achieved. With the integration of machine learning algorithms into waveform data analysis, a wide operation spectrum range of 350–750 nm is available with a 100% accuracy rate. The restoration error has been lowered to less than 0.1% for light intensity. This work offers valuable insights for advancing perovskite applications in areas such as wavelength identification and spectrum imaging.

在没有光学和机械结构的情况下，通过单个光电探测器破译光场内的复合信息是具有挑战性的。难点在于从一维光电流中提取多维光学信息。基于信息重构的新兴光电探测器具有潜力，但它们仅提取光响应电流幅度（响应度矩阵）中包含的信息，忽略了载流子动力学驱动的响应边缘中的隐藏信息。在此，通过调整钙钛矿光电二极管中吸收层的厚度和界面电场强度，延长了不同波长光子激发的载流子的传输和弛豫时间，最大化明暗中边缘波形的光谱丰富度过渡过程。首次在不需要额外光学和电气元件的情况下，实现了光强度和波长的二维信息的重构。通过将机器学习算法集成到波形数据分析中，可实现 350–750 nm 的宽操作光谱范围，且准确率达 100%。光强度恢复误差已降至0.1%以下。这项工作为推进钙钛矿在波长识别和光谱成像等领域的应用提供了宝贵的见解。