Guaranteeing high-quality test solutions for Spin-Transfer Torque Magnetic RAM (STT-MRAM) is a must to speed up its high-volume production. A high test quality requires maximizing the fault coverage. Detecting permanent faults is relatively simple compared to intermittent faults; the latter are faults (caused by non-environmental conditions) that appear and disappear as a function of time, and are therefore hard to detect. Testing for such faults in STT-MRAMs is even worse considering the Magnetic Tunneling Junction inherent property ‘intrinsic switching stochasticity’, which results in inevitable random write errors. This paper presents a novel Design-for-Testability (DFT) scheme for detecting intermittent faults in STT-MRAMs; it is based on monitoring the write current. The strength of the write current is inversely correlated to the write error rate; when the write current is smaller than the specification, the device is considered faulty. A reduction in the write current can be caused by any defect in the write path of the memory (e.g., interconnects and contacts). Simulation results based on industrial design show that applying DFT yields a superior coverage of intermittent faults compared to functional test methods, such as march tests.@en

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.

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