特别会议-新兴的基于忆阻器的存储器和CIM架构:测试,维修和良率分析

R. Bishnoi, Lizhou Wu, M. Fieback, Christopher Münch, S. Nair, M. Tahoori, Ying Wang, Huawei Li, S. Hamdioui
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引用次数: 7

摘要

新兴的基于忆阻器的架构对于数据密集型应用很有希望,因为它们可以提高计算效率,解决数据传输瓶颈,同时利用其正常关闭/立即打开的属性提供高能效。然而,与传统存储技术相比,它们的存储设备更容易受到制造缺陷的影响,因为它们是用新材料制造的,需要不同的制造工艺。因此,为了确保这些技术的正确功能,有必要拥有准确的故障建模以及具有高测试覆盖率的适当测试方法。在本文中,我们提出了技术特定的细胞级缺陷建模,准确的故障分析和良率改进的解决方案,用于基于忆阻器的存储器和内存计算(CIM)架构。我们的总体贡献涵盖了三个抽象层次,即设备、体系结构和系统。首先,我们提出了一种设备感知测试方法,在该方法中,我们引入了一个关键的设备级特征来开发准确的缺陷模型。其次,我们展示了一个考虑可靠性和由于参数变化导致的永久故障的记忆电阻阵列的良率分析框架,并探索了容错解决方案。第三,针对机器学习应用中新兴的CIM设备,提出了一种轻量级的在线测试和维修方案。
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Special Session – Emerging Memristor Based Memory and CIM Architecture: Test, Repair and Yield Analysis
Emerging memristor-based architectures are promising for data-intensive applications as these can enhance the computation efficiency, solve the data transfer bottleneck and at the same time deliver high energy efficiency using their normally-off/instant-on attributes. However, their storing devices are more susceptible to manufacturing defects compared to the traditional memory technologies because they are fabricated with new materials and require different manufacturing processes. Hence, in order to ensure correct functionalities for these technologies, it is necessary to have accurate fault modeling as well as proper test methodologies with high test coverage. In this paper, we propose technology specific cell-level defect modeling, accurate fault analysis and yield improvement solutions for memristor-based memory as well as Computation-In-Memory (CIM) architectures. Our overall contributions cover three abstraction levels, namely, device, architecture and system. First, we propose a device-aware test methodology in which we have introduced a key device-level characteristic to develop accurate defect model. Second, we demonstrate a yield analysis framework for memristor arrays considering reliability and permanent faults due to parametric variations and explore fault-tolerant solutions. Third, a lightweight on-line test and repair schemes is proposed for emerging CIM devices in machine learning applications.
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