Adaptive immunomorphic hardware based on organic semiconductors and oxidized MXene heterostructures for feature information recognition

Abstract

Neuromorphic computing with brain-like functions has become one of the important strategies for the von Neumann bottleneck. However, current artificial neuromorphic systems are mainly based on volatile or non-volatile synaptic devices, which limit the flexibility and computational efficiency of neuromorphic computing systems. Here, we report an adaptive immunomorphic hardware based on the heterostructure of MXene-TiO2 complexes and organic semiconductors. The hardware has photon-triggered synaptic plasticity for accurate recognition and electrically triggered non-volatile retention for effective preservation of weight values. As a result, the retraining time and power consumption of the hardware can be reduced by 95% and 96%, respectively. Moreover, the array system expanded to 5 × 5 can extract special information from complex signals within 0.2 s, enabling feature information recognition. This work, therefore, provides a new strategy for improving the efficiency of artificial neuromorphic computation and has significant application prospects in intelligent sensing systems and edge computing.