Abstract
In-sensor computing is proposed to reduce energy expenditure and processing latency by unifying sensing and computation within the hardware layer, yet the application under extreme illuminating scenario remains constrained by simultaneously obtaining broadband responsivity, large linear dynamic range and fast response. Here, we report a fully vapor-deposited graded Pb-Sn alloyed perovskite heterojunction photodiode with improved crystal quality. It enables the detection of light from visible to infrared light with a 230 dB linear dynamic range and 33 ns response time. We also develop a wafer-scale imaging processor by integrating the photodiode to a reconfigurable array. With this approach, we demonstrate biomedical detection and spatiotemporal trajectory encoding. The in-sensor processor realizes low-power high-resolution visible to infrared wavelength edge detection, adaptive background suppression under dim light and noise-immune high-speed dynamic imaging. Our results extend the options for in-sensor computing hardware, and thus pave a way toward practical artificial intelligent machine vision.
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The source data that support the plots within this paper are provided with this paper. Other data that support the findings of this paper are available from the corresponding author upon request. Source data are provided with this paper.
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The codes used for LSTM network training are available from the corresponding authors upon request.
References
Wan, T. et al. In-sensor computing: materials, devices, and integration technologies. Adv. Mater. 35, e2203830 (2023).
Sangwan, V. K. & Hersam, M. C. Neuromorphic nanoelectronic materials. Nat. Nanotechnol. 15, 517–528 (2020).
Cho, S. W., Jo, C., Kim, Y.-H. & Park, S. K. Progress of materials and devices for neuromorphic vision sensors. Nano-Micro Lett. 14, 203 (2022).
Zhou, F. & Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 3, 664–671 (2020).
Xiong, Z. et al. Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors. Nat. Commun. 16, 4728 (2025).
Li, T. et al. Reconfigurable, non-volatile neuromorphic photovoltaics. Nat. Nanotechnol. 18, 1303–1310 (2023).
Cui, B. et al. Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision. Nat. Commun. 13, 2022 (1707).
Cai, Y. et al. 8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks. Nat. Commun. 16, 2649 (2025).
Ouyang, B. et al. Bioinspired in-sensor spectral adaptation for perceiving spectrally distinctive features. Nat. Electron. 7, 705–713 (2024).
Pan, X. et al. Parallel perception of visual motion using light-tunable memory matrix. Sci. Adv. 9, eadi4083 (2023).
Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).
Pi, L. et al. Broadband convolutional processing using band-alignment-tunable heterostructures. Nat. Electron. 5, 248–254 (2022).
Huang, H. et al. In-sensor compressing via programmable optoelectronic sensors based on van der Waals heterostructures for intelligent machine vision. Nat. Commun. 16, 3836 (2025).
Jiang, T. et al. Tetrachromatic vision-inspired neuromorphic sensors with ultraweak ultraviolet detection. Nat. Commun. 14, 2281 (2023).
Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5, 519–525 (2022).
Zhou, S. et al. Dual-mode photodetectors mimicking retinal rod and cone cells for high dynamic range image sensor. Laser Photonics Rev. 19, 2402192 (2025).
Liao, F. et al. Bioinspired in-sensor visual adaptation for accurate perception. Nat. Electron. 5, 84–91 (2022).
Morteza Najarian, A. et al. Sub-millimetre light detection and ranging using perovskites. Nat. Electron. 5, 511–518 (2022).
Zhou, Y. et al. Self-powered perovskite photon-counting detectors. Nature 616, 712–718 (2023).
Tsarev, S. et al. Vertically stacked monolithic perovskite colour photodetectors. Nature 642, 592–598 (2025).
Ollearo, R. et al. Ultralow dark current in near-infrared perovskite photodiodes by reducing charge injection and interfacial charge generation. Nat. Commun. 12, 7277 (2021).
Ma, Y. et al. Day-Night imaging without Infrared Cutfilter removal based on metal-gradient perovskite single crystal photodetector. Nat. Commun. 15, 7516 (2024).
Im, J., Stoumpos, C. C., Jin, H., Freeman, A. J. & Kanatzidis, M. G. Antagonism between spin–orbit coupling and steric effects causes anomalous band gap evolution in the perovskite photovoltaic materials CH3NH3Sn1–xPbxI3. J. Phys. Chem. Lett. 6, 3503–3509 (2015).
Reo, Y. et al. Vapour-deposited high-performance tin perovskite transistors. Nat. Electron. 8, 403–410 (2025).
Chen, X. et al. Improved conductivity of 2D perovskite capping layer for realizing high-performance 3D/2D heterostructured hole transport layer-free perovskite photovoltaics. ACS Nano 19, 4299–4308 (2025).
Ortiz-Cervantes, C., Román-Román, P. I., Vazquez-Chavez, J., Hernández-Rodríguez, M. & Solis-Ibarra, D. Thousand-fold conductivity increase in 2D perovskites by polydiacetylene incorporation and doping. Angew. Chem. Int. Ed. Engl. 130, 14078–14082 (2018).
Xu, P. & Liu, F. First-principles investigation on the photovoltaic properties of lead free earth abundant (CH3NH3)2SnI6 perovskite. J. Appl. Phys. 129, 125701 (2021).
Funabiki, F., Toda, Y. & Hosono, H. Optical and electrical properties of perovskite variant (CH3NH3)2SnI6. J. Phys. Chem. C 122, 10749–10754 (2018).
Derbel, N. & Kanoun, O. (eds.). Advanced Methods for Human Biometrics (Springer, Cham, 2021).
FAULDS, H. Poroscopy: the Scrutiny of Sweat-pores for Identification. Nature 91, 635–636 (1913).
Sheng, W., Zheng, Z. & Zhu, H. A novel approach to palm vein image segmentation combining multi-scale convolution and swin-transformer networks. Sci. Rep. 15, 17539 (2025).
Ren, H., Sun, L., Fan, X., Cao, Y. & Ye, Q. IIS-FVIQA: finger vein image quality assessment with intra-class and inter-class similarity. Pattern Recognit. 158, 111056 (2025).
Hou, B., Zhang, H. & Yan, R. Finger-vein biometric recognition: a review. IEEE Trans. Instrum. Meas. 71, 1–26 (2022).
Kim, H.-L. & Kim, S.-H. Pulse wave velocity in atherosclerosis. Front. Cardiovasc. Med. 6, 41 (2019).
Pereira, T., Correia, C. & Cardoso, J. Novel methods for pulse wave velocity measurement. J. Med. Biol. Eng. 35, 555–565 (2015).
McCombie, D. B., Reisner, A. T. & Asada, H. H. Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp 3521–3524.
PM, N., Karthik, S., Joseph, J. & Sivaprakasam, M. Experimental validation of dual PPG local pulse wave velocity probe. In IEEE International Symposium on Medical Measurements and Applications (MeMeA). pp 408–413 (2017).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
van Houdt, G., Mosquera, C. & Nápoles, G. A review on the long short-term memory model. Artif. Intell. Rev. 53, 5929–5955 (2020).
Baker, M. A. et al. Femtosecond laser ablation (fs-LA) XPS—a novel XPS depth profiling technique for thin films, coatings and multi-layered structures. Appl. Surf. Sci. 654, 159405 (2024).
Chandler, C. W. et al. Femtosecond laser ablation (fs-LA) XPS depth profiling of lead halide perovskite thin film solar cells. Surf. Interface Anal. 57, 246–252 (2025).
Fang, Y., Armin, A., Meredith, P. & Huang, J. Accurate characterization of next-generation thin-film photodetectors. Nat. Photon. 13, 1–4 (2019).
Pecunia, V. et al. Guidelines for accurate evaluation of photodetectors based on emerging semiconductor technologies. Nat. Photon. 19, 1178–1188 (2025).
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 62325404 and 62174072) and the National Key Research and Development Program of China (Grant No. 2022YFA1203500). The authors are very grateful to Dr. Qingqin Ge and Dr. Paul Mack at Thermo Fisher Scientific (China) Co., Ltd. for the XPS measurements and the helpful discussions. The authors gratefully acknowledge the assistance of Lin Wang from the Analytical and Testing Center of Jinan University for the XPS analysis.
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Z.Z. and W.X. conceived the concept. T.S., W.X., and X.W. supervised the project. Z.Z. designed, fabricated, and measured the devices and circuits. Y.L. characterized the films, and W.L. conducted the XPS characterization. Z.Z. and Y.L. developed the imaging system and motion trajectory experimental platform. Y.Z. (Yongjian Zheng), N.P., Y.Z. (Yujian Zheng), D.L., H.L., and J.C. analyzed the data. Z.Z., T.S., X.W., and W.X. wrote the paper. All the authors discussed the results and implications and reviewed the paper.
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Nature Communications thanks Su-Ting Han, Xiangyue Meng and Ye Zhou for their contribution to the peer review of this work. A peer review file is available.
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Zhan, Z., Lu, Y., Zheng, Y. et al. Extreme Illuminated Vision Processing with a Graded Alloyed Perovskite In-sensor Computing Network. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71638-y
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DOI: https://doi.org/10.1038/s41467-026-71638-y
