Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing

Abstract

Ferroelectric (FE) materials are key to advancing electronic devices owing to their non-volatile properties, rapid state-switching abilities, and low-energy consumption. FE-based devices are used in logic circuits, memory-storage devices, sensors, and in-memory computing. However, the primary challenge in advancing the practical applications of FE-based memory is its reliability. To address this problem, a novel polarization pruning (PP) method is proposed. The PP is designed to eliminate weakly polarized domains by applying an opposite-sign pulse immediately after a program or erase operation. Significant improvements in the reliability of ferroelectric devices are achieved by reducing the depolarization caused by weakly polarized domains and mitigating the fluctuations in the ferroelectric dipole. These enhancements include a 25% improvement in retention, a 50% reduction in read noise, a 45% decrease in threshold voltage variation, and a 72% improvement in linearity. The proposed PP method significantly improves the memory storage efficiency and performance of neuromorphic systems. This study introduces Polarization Pruning (PP) to address reliability issues in hafnium-based ferroelectric devices. PP aligns ferroelectric domains by adding a weak counterpulse after conventional pulses, enhancing retention by 20-25%, reducing read noise by 50%, variability by 45%, and nonlinearity by 72%. This advancement significantly benefits nonvolatile memory and AI hardware, offering novel a solution for more reliable ferroelectric devices. image