Presentation
Asymmetric Predictive Testing for Aging in SRAMs
DescriptionTo avoid corruption of user data, predictive testing methods have been proposed to identify SRAMs likely to fail in the near future due to aging. These methods use aggressive operating conditions, e.g., adjustments to wordline voltages or the power supply voltage, that are calibrated to provide high coverage of SRAMs likely to fail in the near future, but end up with some over-testing, i.e., spuriously identifying some chips as likely to fail.
We first present our study which discovered that a large fraction of over-tested chips fail due to read faults triggered during read-1 operations. Our analysis identifies asymmetric aging in SRAM cells, which are more likely to store zeros, as the root cause for this. We build on this discovery to propose an asymmetric predictive testing method which performs writes using normal voltages, read-0 at aggressive voltages, and read-1 at less aggressive voltages. We demonstrate that this method significantly reduces over-testing by over 3x to 5x, for low limits on under-testing. We also propose and use a new statistical sampling and simulation method to enable fast convergence and accurate evaluation of asymmetric predictive testing.
We first present our study which discovered that a large fraction of over-tested chips fail due to read faults triggered during read-1 operations. Our analysis identifies asymmetric aging in SRAM cells, which are more likely to store zeros, as the root cause for this. We build on this discovery to propose an asymmetric predictive testing method which performs writes using normal voltages, read-0 at aggressive voltages, and read-1 at less aggressive voltages. We demonstrate that this method significantly reduces over-testing by over 3x to 5x, for low limits on under-testing. We also propose and use a new statistical sampling and simulation method to enable fast convergence and accurate evaluation of asymmetric predictive testing.
Event Type
Research Manuscript
TimeTuesday, June 241:30pm - 1:45pm PDT
Location3006, Level 3