Interface Charge Engineering in Ferroelectric Neuristors for a Complete Machine Vision System.

The rapid advancement of artificial intelligence has driven the demand for hardware solutions of neuromorphic pathways to effectively mimic biological functions of the human visual system. However, current machine vision systems (MVSs) fail to fully replicate retinal functions and lack the ability to update weights through all-optical pulses. Here, by employing rational interface charge engineering via varying the charge trapping layer thickness of PMMA, we determine that the ferroelectric polarization of our ferroelectric neuristors can be flexibly manipulated through light or electrical pulses. This capability enables dynamic modulation of the device's optoelectronic characteristics, facilitating a complete MVS. As front-end sensors, devices with the thickest PMMA (∼32 nm) demonstrate autonomous light adaptation while those with the thinnest PMMA (∼2 nm) exhibit bidirectional photoresponse characteristics akin to those of bipolar cells. Furthermore, as components of a back-end processor, the conductances of these devices with a moderate thickness (∼12 nm) can be updated linearly through all-optical pulses. Our MVS, constructed with these neuristors, achieved an impressive recognition accuracy of 93% in handwritten digit recognition tasks under extreme lighting conditions. This work offers an effective strategy for the development of energy-efficient and highly integrated intelligent MVSs.