Freely switching between ferroelectric and resistive switching in Hf 0.5 Zr 0.5 O 2 films and its application on high accuracy on-chip deep neural networks

The Hf 0.5 Zr 0.5 O 2 (HZO)-based ferroelectric field-effect transistor (FeFET) synapse is a promising candidate for at-scale deep neural network (DNN) applications, because of its high symmetry, great accuracy and fast operation speed. However, the degradation of the remanent polarization ( P r ) over time caused by the depolarization field has not been effectively resolved, greatly affecting the accuracy of the trained DNN. In this study, we demonstrate a ferroelectric (FE)-resistive switching (RS) switchable synapse using the FE mode for high-speed weight training and the RS mode for stable weight storage to overcome accuracy degradation. The FE-RS hybrid characteristic is accomplished by an HZO-based metal-ferroelectric-metal (MFM) capacitor with asymmetric electrodes, and the best FE endurance, as well as the most reliable RS behavior, is demonstrated by testing several electrodes materials. High memory windows are achieved in both FE and RS modes. Through this design, excellent accuracy is maintained over time, as verified by network simulation.