Special Session - Emerging Memristor Based Memory and CIM Architecture

Test, Repair and Yield Analysis

Conference Paper (2020)
Author(s)

Rajendra Bishnoi (TU Delft - Computer Engineering)

Lizhou Wu (TU Delft - Computer Engineering)

Moritz Fieback (TU Delft - Computer Engineering)

Christopher Münch (Karlsruhe Institut für Technologie)

Sarath Mohanachandran Nair (Karlsruhe Institut für Technologie)

Mehdi B. Tahoori (Karlsruhe Institut für Technologie)

Ji Yin Wang (Chinese Academy of Sciences)

Huawei Li (Chinese Academy of Sciences)

S. Hamdioui (TU Delft - Quantum & Computer Engineering)

Research Group
Computer Engineering
DOI related publication
https://doi.org/10.1109/VTS48691.2020.9107595
More Info
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Publication Year
2020
Language
English
Research Group
Computer Engineering
Volume number
2020-April
ISBN (electronic)
9781728153599

Abstract

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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