Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed-Weight Plasticity for Augmenting Perception

Organic photoelectric neuromorphic devices that mimic the brain are widely explored for advanced perceptual computing. However, current individual neuromorphic synaptic devices mainly focus on utilizing linear models to process optoelectronic signals, which means that there is a lack of effective response to nonlinear structural information from the real world, severely limiting the computational efficiency and adaptability of networks to static and dynamic information. Here, a feedforward photoadaptive organic neuromorphic transistor with mixed-weight plasticity is reported. By introducing the potential of the space charge to couple gate potential, photoexcitation, and photoinhibition occur successively in the channel under the interference of constant light intensity, which enables the device to transform from a linear model to a nonlinear model. As a result, the device exhibits a dynamic range of over 100 dB, exceeding the currently reported similar neuromorphic synaptic devices. Further, the device achieves adaptive tone mapping within 5 s for static information and achieves over 90% robustness recognition accuracy for dynamic information. Therefore, this work provides a new strategy for developing advanced neuromorphic devices and has great potential in the fields of intelligent driving and brain-like computing. This work proposes a feedforward adaptive organic neuromorphic transistor with mixed-weight plasticity (MP-ONH) that transitions from linear mode to nonlinear mode based on light intensity. It achieves a dynamic range of 100 dB for light intensity, surpassing current similar synaptic devices. It also enables adaptive tone mapping in 5 s for static information and achieves over 90% accuracy in robust recognition of dynamic information.image