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Convolutional Compaction-Based MRAM Fault Diagnosis

A new memory fault diagnosis scheme capable of handling STT-MRAM-specific error rates in an efficient manner.

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Abstract:

“Spin-transfer torque magnetoresistive random-access memories (STT-MRAMs) are gradually superseding conventional SRAMs as last-level cache in System-on-Chip designs. Their manufacturing process includes trimming a reference resistance in STT-MRAM modules to reliably determine the logic values of 0 and 1 during read operations. Typically, an on-chip trimming routine consists of multiple runs of a test algorithm with different settings of a trimming port. It may inherently produce a large number of mismatches. Diagnosis of such a sizeable volume of errors by means of existing memory built-in self-test (MBIST) schemes is either infeasible or a time-consuming and expensive process. In this paper, we propose a new memory fault diagnosis scheme capable of handling STT-MRAM-specific error rates in an efficient manner. It relies on a convolutional reduction of memory outputs and continuous shifting of the resultant data to a tester through a few output channels that are typically available in designs using an on-chip test compression technology, such as the embedded deterministic test. It is shown that processing the STT-MRAM output by using a convolutional compactor is a preferable solution for this type of applications, as it provides a high diagnostic resolution while incurring a low hardware overhead over traditional MBIST logic.”

 

Find the technical paper here.

 

B. Grzelak, M. Keim, A. Pogiel, J. Rajski and J. Tyszer, “Convolutional Compaction-Based MRAM Fault Diagnosis,” 2021 IEEE European Test Symposium (ETS), 2021, pp. 1-6, doi: 10.1109/ETS50041.2021.9465464.



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