Searcharxiv⌕ Search

arXiv subjects

Jianyuan Zhao

Publications and source records attributed to Jianyuan Zhao.

2 recordsLinked to original sources

Decoupling effects of the resistive-switching behavior on the polarization reversal in ultrathin ferroelectric Hf0.5Zr0.5O2 films

HfO2-based ferroelectric films have attracted considerable attention as their nanoscale ferroelectricity and compatibility with cmos technology, fulfilling demands of emerging memory technologies. However, as films scale down, resistive-switching behavior becomes increasingly pronounced, intricately intertwining with the polarization-switching process and affecting ferroelectric switching factors often overlooked yet crucial for device performance optimization. By characterizing resistive-switching behavior and oxygen vacancy motion using tailored electric pulse schemes, we decouple the resistive-switching behavior from the overall switching process in ultrathin ferroelectric HZO films, which would otherwise erroneously inflate polarization values and increase coercive fields. Building on this, we elucidate endurance degradation mechanisms from dual perspectives of resistive switching and defect migration. Furthermore, we demonstrate the mitigated resistive switching activity by designing HfO2-based devices with symmetric oxide electrodes, achieving reduced coercive fields and improved cycling performances. This work provides crucial insights into the origins of inflated polarizations and reliability challenges in HfO2-based devices while offering a viable strategy to enhance ferroelectric properties for advanced memory applications.

cond-mat.mtrl-sci↗

Cryogenic in-memory computing using magnetic topological insulators

Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and CMOS technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum physics-based novel computing schemes.

cond-mat.mes-hall↗