(Invited) Physical Understanding of HfZrO2/Si FeFET Memory and Its AI Applications

Abstract

Si-friendly HfO2-based FeFETs have been well recognized as a technology booster for future integrated memory applications, which are expected to enhance the energy efficiency of AI computation. In this paper, we present our recent studies on the understanding of the coupling between polarization, inversion-layer charges, and carrier traps in HfZrO2 (HZO)/Si FeFET memory. Discrimination of polarization, inversion carriers, and trapped carriers is realized by combining P-V, quasi-split C-V, and Hall measurements of FeFETs. Polarization and electron/hole-trap interactions significantly affect the performance and characteristics of HZO/Si FeFETs. A large number of electron traps can enhance polarization and the memory window of n-FeFETs, while there are almost no hole traps, leading to the smaller memory window of p-FeFETs. We have also proposed a new scheme of physical reservoir computing with parallel data processing for speech recognition by using HZO/Si FeFETs. A high classification accuracy of 95.9 % has been experimentally demonstrated in the task to classify the audio waveforms of ‘0’ to ‘9’ spoken digits.