神经形态工程学
材料科学
光电子学
光电二极管
异质结
频道(广播)
动态随机存取存储器
偏压
晶体管
逻辑门
人工神经网络
CMOS芯片
电导
突触
瞬态(计算机编程)
记忆电阻器
计算机科学
氧化物
纳米电子学
欧姆接触
放松(心理学)
纳米技术
可塑性
铟
电容
阈值电压
电阻随机存取存储器
电阻式触摸屏
作者
Ahasan Ullah,Roshell Lamug,Tasnim Sarker,Xueqiao Zhang,Andrew Ensinger,Lizhong Chen,Oksana Ostroverkhova,Li‐Jing Cheng
摘要
ABSTRACT We present a neuromodulator‐inspired organic–inorganic hybrid phototransistor that enables programmable optoelectronic memory and synaptic plasticity via dual optical–electrical modulation. The device integrates a tetracene‐based photoactive polymer blend with an indium gallium zinc oxide (IGZO) thin‐film transistor, forming a type‐II heterojunction where photogenerated electrons transfer to the IGZO channel while holes are stored in deep trap states within the organic layer. These trapped charges induce persistent photogating, modulating channel conductance analogous to synaptic potentiation. Trap‐dynamics modeling reveals that deep traps govern long‐term retention, whereas shallow traps allow rapid adaptation. Gate bias further reshapes the trapped‐charge distribution and tunes trapping–detrapping kinetics, achieving programmable transitions between short‐term plasticity and long‐term memory. Negative gate bias reinforces conductance retention (dopamine‐like potentiation), whereas positive bias accelerates relaxation (serotonin‐like depression), enabling polarity‐controlled switching between analog learning, long‐term storage, and controlled forgetting. Operating below 5 µW·cm −2 , the device supports weak‐light imaging (0.5 µW·cm −2 ), memory retention beyond 5000 s, and spatiotemporal trajectory tracking via transient charge storage. A device‐calibrated artificial neural network using differential synapse pairs achieves >90% MNIST classification accuracy with near‐linear weight updates over hundreds of cycles. These results demonstrate an energy‐efficient platform unifying sensing, memory, and computation for neuromodulator‐inspired in‐sensor neuromorphic processing.
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