Pd/HfO2阻变器件无需预成形，低功耗多比特存算一体
PdNeuRAM: forming-free, multi-bit Pd/HfO2 ReRAM for energy-efficient neuromorphic computing
- 利用Pd-O-Hf结构实现室温电荷重分布，省去昂贵预成形步骤
- 支持多比特存储，读写功耗降低73%和43%，提升能效
- 适合构建低功耗神经形态计算系统，尤其适用于边缘设备
阻变存储器在低功耗计算中前景广阔，但存在电阻漂移和固有离散性等挑战。对于典型的丝状阻变存储器，必需的电成形步骤带来高功耗、面积开销大及耐久性下降问题。本研究提出新型基于HfO2的无成形阻变器件——PdNeuRAM，可在低电压下工作，支持多比特功能，并显著降低离散性。通过深入材料表征发现：Pd与HfO2的本征亲和力形成Pd-O-Hf结构，可在室温下促进电荷重分布，从而消除电成形需求。所制备器件可实现可调电阻态，用于密集存储，且在脉冲神经网络（SNN）中编程与读取功耗分别降低43%和73%。该研究揭示了新机制，为高效低成本阻变器件提供了可行路径。
原文摘要 · Abstract (English)
Memristor technology shows great promise for energy-efficient computing, yet it grapples with challenges like resistance drift and inherent variability. For filamentary Resistive RAM (ReRAM), one of the most investigated types of memristive devices, the expensive electroforming step required to create conductive pathways results in increased power and area overheads and reduced endurance. In this study, we present novel HfO2-based forming-free ReRAM devices, PdNeuRAM, that operate at low voltages, support multi-bit functionality, and display reduced variability. Through a deep understanding and comprehensive material characterization, we discover the key process that allows this unique behavior: a Pd-O-Hf configuration that capitalizes on Pd innate affinity for integrating into HfO2. This structure actively facilitates charge redistribution at room temperature, effectively eliminating the need for electroforming. Moreover, the fabricated ReRAM device provides tunable resistance states for dense memory and reduces programming and reading energy by 43% and 73%, respectively, using spiking neural networks (SNN). This study reveals novel mechanistic insights and delineates a strategic roadmap for the realization of power-efficient and cost-effective ReRAM devices.
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