Mahan Rezaei, Abdolah Amirany, M. H. Moaiyeri, Kian Jafari
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引用次数: 0
摘要
本文介绍了一种创新的非易失性联想存储器(AM),它利用自旋电子突触,采用磁隧道结(MTJ),结合使用碳纳米管场效应晶体管(CNTFET)构建的神经元。我们提出的设计在面积优化方面取得了重大进展,并优于之前的设计。我们采用基于 MTJ 的自旋电子器件,因为它们具有可靠的可重构性和非挥发性等显著特性。同时,CNTFET 还能有效解决传统 MOSFET 长期存在的局限性。在这项工作中,我们提出的设计经过了严格的模拟,考虑到了工艺变化。结果表明,我们的 AM 系统非常接近其理想的数学模型,即使在工艺变化很大的情况下也是如此。此外,我们还研究了隧道磁阻 (TMR) 对我们提出的 AM 系统性能的影响。研究结果表明,即使 TMR 低至 100%,我们的设计也能达到甚至超过 TMR 为 300% 的同类产品的性能。从制造的角度来看,这一成就具有深远的意义,因为制造具有高 TMR 值的 MTJ 既复杂又昂贵。总之,我们的新型 AM 系统代表了新兴技术的重大突破,它利用了自旋电子突触和先进碳纳米管晶体管的独特优势,同时有力地应对了性能和可变性方面的挑战。
A reliable non‐volatile in‐memory computing associative memory based on spintronic neurons and synapses
This article introduces an innovative non‐volatile associative memory (AM) that leverages spintronic synapses, employing magnetic tunnel junctions (MTJ) in conjunction with neurons constructed using carbon nanotube field‐effect transistors (CNTFETs). Our proposed design represents a significant advancement in area optimization and outperforms prior designs. We adopt MTJ‐based spintronic devices due to their remarkable attributes, including dependable reconfigurability and nonvolatility. Simultaneously, CNTFETs effectively address the longstanding limitations traditionally associated with MOSFETs. In this work, our proposed design undergoes rigorous simulations that account for process variations. The results demonstrate that our AM system closely approximates its ideal mathematical model, even with significant process variations. Furthermore, we investigate the impact of Tunnel Magnetoresistance (TMR) on the performance of our proposed AM system. Our investigations reveal that, even with a TMR as low as 100%, our design matches and often surpasses the performance of its counterparts operating with a TMR of 300%. This achievement holds profound significance from a fabrication standpoint, as fabricating MTJs with high TMR values can be intricate and costly. Overall, our novel AM system represents a significant breakthrough in emerging technologies, harnessing the unique strengths of spintronic synapses and advanced carbon nanotube transistors while robustly addressing challenges in performance and variability.