AI 中文总结
本研究针对模拟ReRAM交叉阵列内存内训练的权重更新干扰难题，开发了350nm工艺的抗干扰ReRAM器件，验证了其在内存深度学习加速器全并行权重更新中的应用潜力。
AI 中文摘要
采用交叉阵列架构的阻变存储器（ReRAM）技术在模拟AI加速器硬件中具有巨大潜力，可实现内存内推理与训练。近期进展已通过将计算密集型训练工作负载卸载至片外数字处理器，成功实现推理加速，但训练算法的内存内加速对于构建更具可持续性和能效的AI至关重要，目前仍处于研究早期阶段。本研究聚焦于模拟ReRAM阵列的内存内训练加速，解决全并行权重更新过程中的关键挑战：交叉点器件的权重值干扰问题。提出了基于350nm硅工艺的ReRAM器件解决方案，利用HfOx层内的纳米级导电丝形成的阻变导电金属氧化物（CMO）。该器件不仅具备60纳秒的快速非易失性模拟开关特性，还展现出出色的抗更新干扰能力，可承受超过10万次脉冲。通过COMSOL Multiphysics仿真分析了该ReRAM的抗干扰性能，对导电丝诱导的热电能量集中进行建模，该现象导致器件对输入电压幅度呈现高度非线性响应。还在后端集成的ReRAM阵列芯片上展示了无干扰的并行权重映射。最后，全面的硬件感知神经网络仿真验证了本研究的ReRAM在具备全并行权重更新能力的内存深度学习加速器中的应用潜力。
英文摘要
Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and training. Recent developments have successfully demonstrated inference acceleration by offloading compute-heavy training workloads to off-chip digital processors. However, in-memory acceleration of training algorithms is crucial for more sustainable and power-efficient AI, but still in an early stage of research. This study addresses in-memory training acceleration using analog ReRAM arrays, focusing on a key challenge during fully parallel weight updates: disturbances of the weight values in cross-point devices. A ReRAM device solution is presented on 350 nm silicon technology, utilizing a resistive switching conductive metal oxide (CMO) formed on a nanoscale conductive filament within a HfOx layer. The devices not only exhibit 60 ns fast, non-volatile analog switching, but also demonstrates outstanding resilience to update disturbances, enduring over 100k pulses. The disturbance tolerance of the ReRAM is analyzed using COMSOL Multiphysics simulations, modeling the filament-induced thermoelectric energy concentration that results in a highly nonlinear device responses to input voltage amplitudes. Disturbance-free parallel weight mapping is also demonstrated on the back-end-of-line integrated ReRAM array chip. Finally, comprehensive hardware-aware neural network simulations validate the potential of our ReRAM for in-memory deep learning accelerators capable of fully parallel weight updates.
Journal refAdv. Sci. 13, no. 4 (2026): e04578
