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Self-Adaptive Infrared Vision via Neural-Controlled Gain Compression in a Single Photodetector
Yuxin Song1,2, Xin Li1,2, Junzhe Gu1,2
1State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
None:
Biological vision relies on eye-mediated gain control to adapt across lighting conditions-but remains fundamentally blind to infrared wavelengths and polarization. Here, we report a neuromorphic photodetector that not only emulates this self-adaptive functionality, but surpasses human vision by enabling dynamic gain regulation across the infrared-polarization domain. Using a gate-tunable Au/BP/PdSe2 van der Waals heterostructure (vdWH), we achieve eye-like nonlinear gain compression via electrostatic barrier reconfiguration, which enables dynamic modulation of both the response area and responsivity. Integrated with a neural-network-based microcontroller, the system forms a device-level closed-loop that autonomously adjusts optical gain in real time. This expands the linear dynamic range (LDR) by three orders of magnitude, reaching ∼80 dB at 1550 nm, with sub-millisecond response and intrinsic polarization sensitivity (PR > 10)-all without external optics or analog circuitry. These results establish a scalable, intelligent optoelectronic platform that augments biological perception and advances chip-scale self-adaptive vision for autonomous sensing and edge photonic intelligence.
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