28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs

npj Unconventional Computing Pub Date : 2024-09-02 DOI:10.1038/s44335-024-00008-y

Joao Henrique Quintino Palhares, Nikhil Garg, Pierre-Antoine Mouny, Yann Beilliard, J. Sandrini, F. Arnaud, Lorena Anghel, Fabien Alibart, Dominique Drouin, Philippe Galy

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Abstract

Seeking to circumvent conventional computing bottlenecks, hardware alternatives, from brain-inspired designs to cryogenic quantum systems, necessitate integrating emerging non-volatile memories. Yet, the immaturity and unreliability of cryogenic-compatible memories hinder scalable computing advancements. This study characterizes 28 nm FD-SOI substrate-embedded Ge-rich Ge2Sb2Te5 phase change memories (ePCMs) down to 12 K to overcome these hurdles. It reveals that ePCMs is cryogenic compatible and can encode multiple resistance states with minimal drift, essential for advanced computing solutions. Through simulations, the ePCM’s impact on a spiking neural network (SNN) performing MNIST classification is evaluated. The SNN maintains high accuracy for extended periods of 2 years at cryogenic temperatures, while an accuracy drop of 10.8% is observed at room temperature. These results highlight the potential of multilevel ePCMs in brain-inspired cryogenic computing applications, offering a promising avenue for the evolution of unconventional computing systems.

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用于低温 SNN 的 28 纳米 FDSOI 嵌入式 PCM 在 12 K 时漂移接近于零

为了规避传统计算的瓶颈，从大脑启发设计到低温量子系统等硬件替代方案都需要集成新兴的非易失性存储器。然而，低温兼容存储器的不成熟和不可靠阻碍了可扩展计算的发展。本研究对 28 纳米 FD-SOI 衬底嵌入富 Ge Ge2Sb2Te5 相变存储器（ePCMs）进行了表征，其温度可低至 12 K，从而克服了这些障碍。研究表明，ePCMs 与低温兼容，能以最小漂移编码多种电阻状态，这对先进计算解决方案至关重要。通过模拟，评估了 ePCM 对执行 MNIST 分类的尖峰神经网络 (SNN) 的影响。在低温条件下，SNN 可在长达 2 年的时间内保持较高的准确度，而在室温条件下，准确度下降了 10.8%。这些结果凸显了多级 ePCM 在大脑启发的低温计算应用中的潜力，为非常规计算系统的发展提供了一条前景广阔的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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npj Unconventional Computing

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