Programming arbitrary analog conductance states of memristors in one step

Engineering notes

Conclusion This work significantly improves the analog programming speed of memristor devices and offers a critical advancement for leveraging resistive memory in data-intensive storage-class applications and AI hardware.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Introduction The exponential growth of data-intensive workloads, spanning large-scale neural inference and scientific computing, has exposed the inherent bottlenecks of conventional von Neumann architecture. In-memory analog computing, which leverages memristive crossbar arrays, has emerged as a compelling alternative by enabling highly parallel matrix computations through the exploitation of fundamental physical laws. However, due to the intrinsic stochasticity of resistive switching dynamics, achieving high-precision analog programming becomes difficult and mandates iterative write-verify procedures. These digitally-controlled loops introduce substantial latency and peripheral hardware overhead, undermining the throughput and energy efficiency inherent to analog acceleration. Methods We introduce a novel closed-loop feedback architecture that transforms analog-state programming into a self-regulated physical evolution. Unlike traditional discrete control loops, the proposed circuit utilizes its intrinsic dynamics to continuously sense the discrepancy between the instantaneous device conductance and a predefined target value. This error is converted in real time into a regulated feedback signal that drives the device toward the desired state, automatically halting the programming stimulus once the target is reached. Results Based on the fabricated memristor devices, experiment results show the circuit can achieve analog programming in one step (∼100 ns). Experimental results also validate successful 3-bit analog tuning within 100 ns, regardless of the initial conductance state. The average relative programming error is only about 2.2%. Moreover, a hybrid approach that augments this autonomous feedback with traditional write-verify cycles is adopted to enhance the programming precision. This approach improves overall programming speed by 3.7× compared to the traditional write-verify scheme. Conclusion This work significantly improves the analog programming speed of memristor devices and offers a critical advancement for leveraging resistive memory in data-intensive storage-class applications and AI hardware.

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