Abstract
Real-time sensing and processing of a large amount of tactile information is essential for intelligent robotics and wearable technology. However, physical separation between sensors and processors in the traditional tactile sensing scheme makes these functionalities inaccessible, posing a major roadblock to the rapid advance of skinomorphic electronics. Here, we propose a massively parallel in-sensor skinomorphic computing scheme and demonstrate its promising applications in intelligent tactile perception. This scheme allows for achieving parallel sensing and processing of tactile information directly within sensor. We implement this proposed scheme by fabricating a 32×32 flexible capacitive pressure sensors array with excellent uniformity and endurance, and by cascading the sensors array with a memristive crossbar array. We experimentally demonstrate that the broken pressure patterns of the letter ‘NJU’ loaded on the sensors array can be sensed and restored in parallel, which is inaccessible with previously reported tactile technologies. Moreover, by networking the pressure sensors array with two memristive crossbar arrays, we show that textural features of the loaded complex pressure patterns can be directly extracted in a parallel manner and the tactile information can thus be compressed. Our work opens up an avenue for developing intelligent skins capable of real-time and high-throughput tactile perception.
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Acknowledgements
This work was supported in part by the National Key R&D Program of China under grant 2023YFF1203600 (S.-J.L.), the National Natural Science Foundation of China (62304104 (Y.K.Y.), 62304105 (X.N.Y.)), the Leading-edge Technology Program of Jiangsu Natural Science Foundation (BK20232004 (F.M.)), the Natural Science Foundation of Jiangsu Province (BK20233001), the AI & AI for Science Project of Nanjing University (14380240, 14380242, and 14380005), the Fundamental Research Funds for the Central Universities (14380227, 14380247, 14380250). F.M. and S. J.L. would like to acknowledge support from the AIQ Foundation and the e-Science Center of the Collaborative Innovation Center of Advanced Microstructures. The microfabrication center of the National Laboratory of Solid State Microstructures (NLSSM) is also acknowledged for its technical support. This work has been supported by the New Cornerstone Science Foundation through the XPLORER PRIZE. The work was also supported by the Open Fund of State Key Laboratory of Infrared Physics (Grant No. SITP-SKLIP-ZD-2025-01) and Nanjing University International Collaboration Initiative.
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Y.L., Y.Y., C.W., S.-J.L., and F.M. conceived the concept and designed the experiments. S.-J.L. and F.M. supervised the whole project. Y.L., Y.D., S.W., Z.L., and X.Y. prepared and fabricated the capacitive sensor array. Y.L., X.Y., Y.D., and P.W. performed sensor characterization and pressure response measurements. Y.L., Y.D., and C.W. performed pressure pattern sensing and processing based on the capacitive sensor array. Y.Y., D.K., C.W., and G.-J.R. carried out the simulations. Y.L., Y.Y., C.W., Y.D. and B.C. analyzed the data. Y.L., Y.Y., S.-J.L., and F.M. co-wrote the paper with input from all other authors.
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Li, Y., Yang, Y., Wang, C. et al. Massively parallel in-sensor skinomorphic computing. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71697-1
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DOI: https://doi.org/10.1038/s41467-026-71697-1
