High-Performance Ferroelectric Field-Effect Transistors Based on Ultrathin Indium Oxide for Neuromorphic Computing.

Abstract

The emergence of artificial intelligence has revealed the limitations of traditional von Neumann computing systems in fulfilling the current computational requirements. In-memory computing (IMC) has been generally considered as a promising architecture to break the von Neumann bottleneck, where the FeFET is a strong candidate for developing IMC hardware, but remains challenging. In this work, we demonstrate a complementary metal oxide semiconductor-compatible In₂O₃ FeFET array for neuromorphic computing. The FeFETs exhibit excellent performance, including an ultrahigh on-off ratio (10⁷), large memory window (>6 V), high endurance (10⁷ cycles), long retention time (>10 years), low cycle-to-cycle variation (1.1%), high uniformity, and highly linear and symmetrical long-term potentiation (LTP)/long-term depression (LTD). Finally, we evaluate the performance of fabricated In₂O₃ FeFETs for image classification, and a high overall accuracy of 92.5% is achieved. These results suggest the great potential of In₂O₃ FeFET for constructing IMC hardware for neuromorphic computing.