Accelerating RRAM Testing with a Low-cost Computation-in-Memory based DFT

Conference Paper (2022)
Author(s)

Abhairaj Singh (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Moritz Fieback (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Rajendra Bishnoi (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Filip Bradarić (Student TU Delft)

Anteneh Gebregiorgis (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Rajiv V. Joshi (IBM Thomas J. Watson Research Centre)

Said Hamdioui (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Computer Engineering
DOI related publication
https://doi.org/10.1109/ITC50671.2022.00085 Final published version
More Info
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Publication Year
2022
Language
English
Research Group
Computer Engineering
Pages (from-to)
400-409
ISBN (print)
978-1-6654-6271-6
ISBN (electronic)
978-1-6654-6270-9
Event
2022 IEEE International Test Conference (ITC) (2022-09-23 - 2022-09-30), Anaheim, United States
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Abstract

Emerging non-volatile resistive RAM (RRAM) device technology has shown great potential to cultivate not only high-density memory storage, but also energy-efficient computing units. However, the unique challenges related to RRAM fabrication process render the traditional memory testing solutions inefficient and inadequate for high product quality. This paper presents low-cost design-for-testability (DFT) solutions that augment the testing process and improve the fault coverage. A computation-in-memory (CIM) based DFT is realized to expedite the detection and diagnosis of faults by developing logic designs involving multi-row activation. A novel addressing scheme is introduced to facilitate the diagnosis of faults. Reconfigurable logic designs are developed to detect unique RRAM faults that offer features such as programmable reference generations, period, and voltage of operation. DFT implementations are validated on a post-layout extracted platform and testing sequences are introduced by incorporating the proposed DFTs. Results show that more than 2.3× speedup and better coverage are achieved with 6× area reduction when compared with state-of-the-art solutions.

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