Reconfigurable ferroelectric transistor array for embodied neuromorphic vision with hardware-native sensing, computing, and activation.

Authors

Yuan Li, Zhi-Cheng Zhang, Zhaolong Chen, Fu-Dong Wang, Jian Yao, Hui-Ling Qi, Shu-Han Si, Yue Ding, Lixing Kang, Zhi-Bo Liu, Shuqi Chen, Jian-Guo Tian, Xu-Dong Chen

Published in

Science advances. Volume 12. Issue 29. Pages eaed9436. Jul 17, 2026. Epub Jul 17, 2026.

Abstract

Edge artificial intelligence (AI) and embodied vision call for compact, fast, and energy-efficient hardware that integrates sensing, linear analog computation, and nonlinear activation, while flexibly reallocating these functions as workloads change. However, in-sensor computing (ISC) and in-memory computing (IMC) platforms still implement activation with external peripherals and use fixed functional partitions, which break the analog signal path and restrict system reconfigurability. Here, we report a reconfigurable ferroelectric transistor (Fe-FET) array in which polarization-programmed local fields enable junction-barrier engineering in ambipolar tungsten diselenide (WSe₂) channel. This junction-barrier engineering mechanism co-programs photoresponsivity, multilevel conductance, and tunable nonlinear transport within the same device, allowing each Fe-FET cell to be reassigned among weighted sensing (ISC), linear accumulation (IMC), and hardware-native activation. The array therefore functions as a uniform pool of physical units whose roles and spatial partitions can be dynamically allocated to match task demands without changing the hardware platform. Using this role-reconfigurable platform, we implement an end-to-end analog neuromorphic vision system in which broadband sensing, linear computation, and nonlinear activation are executed natively on the same Fe-FET platform. These results establish a task-adaptive and energy-efficient route toward scalable neuromorphic vision hardware for edge intelligence.

PMID:
42467763
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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