Device Aware Diagnosis for Unique Defects in STT-MRAMs

Conference paper (2023)

Authors

A. Aouichi Electrical Engineering, Mathematics and Computer Science
S. Yuan IMEC-Solliance, Computer Engineering - EEMCS
M. Fieback Computer Engineering - EEMCS
Siddharth Rao IMEC-Solliance
Woojin Kim IMEC-Solliance
Erik Jan Marinissen IMEC-Solliance
Sebastien Couet IMEC-Solliance
M. Taouil Computer Engineering - EEMCS , CognitiveIC
S. Hamdioui CognitiveIC, Computer Engineering - EEMCS
Research Group
Computer Engineering (EEMCS) (TU Delft)
Copyright: © 2023 A. Aouichi, S. Yuan, M. Fieback, Siddharth Rao, Woojin Kim, Erik Jan Marinissen, Sebastien Couet, M. Taouil, S. Hamdioui
DOI: https://doi.org/10.1109/ATS59501.2023.10317952
Test Diagnosis STT-MRAM Device-aware method Unique defect
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Computer Engineering

Abstract

Spin-Transfer Torque Magnetic RAMs (STT-MRAMs) are on their way to commercialization. However, obtaining high-quality test and diagnosis solutions for STT-MRAMs is challenging due to the existence of unique defects in Magnetic Tunneling Junctions (MTJs). Recently, the Device-Aware Test (DA-Test) method has been put forward as an effective approach mainly for detecting unique defecting STT-MRAMs. In this study, we propose a further advancement based on the DA-Test framework, introducing the Device-Aware Diagnosis (DA-Diagnosis) method. This method comprises two steps: a) defining distinctive features of each unique defect by characterization and physical analysis of defective MTJs, and b) utilizing march algorithms to extract distinctive features. The effectiveness of the proposed approach is validated in an industrial setting with real devices and data measurement.