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Sneak-Path Based Test and Diagnosis for 1R RRAM Crossbar Using Voltage Bias Technique

Published: 18 June 2017 Publication History

Abstract

Metal-oxide resistive random access memories with a single memristor device at the crosspoint (1R RRAM) is a promising alternative to next generation storage technology due to their high density, scalability, non-volatility and low power consumption. However, the imperfect fabrication process introduces high defect rates of the nanoscale memristor devices and leads to yield degradation. In addition, sneak-paths occur in 1R RRAM crossbar that can jeaperdize the normal read/write operation. Previous work proposes voltage bias technique to eliminate the sneak-paths. Instead, in the paper, we leverage voltage bias to manipulate various distribution of sneak-paths that can screen one or multiple faults out of a 4 x 4 region of memristors at once, and consequently diagnose the exact location of each faulty memristor within three write-read operations. The SPICE simulation results highlight the effectiveness and efficiency of the proposed test method.

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  1. Sneak-Path Based Test and Diagnosis for 1R RRAM Crossbar Using Voltage Bias Technique

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    cover image ACM Conferences
    DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017
    June 2017
    533 pages
    ISBN:9781450349277
    DOI:10.1145/3061639
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 18 June 2017

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    • (2024)Device-Aware Diagnosis for Yield Learning in RRAMs2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546660(1-6)Online publication date: 25-Mar-2024
    • (2024)Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults2024 6th International Conference on System Reliability and Safety Engineering (SRSE)10.1109/SRSE63568.2024.10772492(222-228)Online publication date: 11-Oct-2024
    • (2023)Robustness for Embedded Machine Learning Using In-Memory ComputingEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-40677-5_17(433-462)Online publication date: 7-Oct-2023
    • (2022)MemChar: Portable Low-Power and Low-Cost Characterization Tool for Memristor DevicesIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2022.314420271(1-9)Online publication date: 2022
    • (2022)NEAT: Nonlinearity Aware Training for Accurate, Energy-Efficient, and Robust Implementation of Neural Networks on 1T-1R CrossbarsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.310985741:8(2625-2637)Online publication date: Aug-2022
    • (2022)Fault Coverage Analysis using Sneak Path based Testing in Memristor Circuits2022 IEEE 31st Microelectronics Design & Test Symposium (MDTS)10.1109/MDTS54894.2022.9826959(1-6)Online publication date: 23-May-2022
    • (2022)A Low-Cost, Nanowatt, Millimeter-Scale Memristive-Vacuum SensorIEEE Sensors Journal10.1109/JSEN.2022.314703522:6(6080-6087)Online publication date: 15-Mar-2022
    • (2022)Accelerating RRAM Testing with a Low-cost Computation-in-Memory based DFT2022 IEEE International Test Conference (ITC)10.1109/ITC50671.2022.00085(400-409)Online publication date: Sep-2022
    • (2022)A Novel Reliability Assessment Scheme for Nano Resistive Random Access Memory (RRAM) TestingAnalog Integrated Circuits and Signal Processing10.1007/s10470-022-02007-0112:1(151-159)Online publication date: 25-Apr-2022
    • (2021)Defect Analysis and Parallel Testing for 3D Hybrid CMOS-Memristor MemoryIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.29828309:2(745-758)Online publication date: 1-Apr-2021
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