Polarization current-based reservoir computing utilizing an anti-ferroelectric-like HfZrO2 capacitor

作者：Shin-Yi Min, Eishin Nako, Ryosho Nakane, Mitsuru Takenaka, Kasidit Toprasertpong, Shinichi Takagi · 发表于：APL Machine Learning · 年份：2025 · DOI：10.1063/5.0255149 · 被引用次数：6 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices

We have experimentally demonstrated the physical reservoir computing by employing the polarization switching current dynamics of an Hf1−xZrxO2 (HZO)-based metal/ferroelectric/metal capacitor with Zr content x = 0, 0.5, and 0.75. The spatial distribution of the crystalline phase of an HZO film reveals that the tetragonal phase is a dominant crystal structure in the HZO film with [Zr] = 75%, resulting in anti-ferroelectric (AFE)-like double polarization switching. Analyses using t-distributed stochastic neighbor embedding (t-SNE) find that the AFE-HZO capacitor effectively transforms the 3-bit time-series input into eight different reservoir output states. In reservoir computing tasks, the AFE-HZO capacitor with [Zr] = 75% achieves improved computational capacities compared with the other MFM capacitors with [Zr] = 0% and 50%. The AFE-HZO capacitor can effectively diversify time-series input signals through dynamic double polarization switching, leading to a more sub-divided and dispersive weight distribution across the adjustable weights in the readout part of our RC system.