In practical NPU (Neutral Processing Unit) designs, SRAM often occupies the largest portion of chip area, consumes the most power, and has the greatest impact on overall reliability. To support highly parallel AI inference workloads with intensive data reuse, NPUs integrate large amounts of on-chip SRAM around compute units as weight buffers, feature-map storage, and local data-access resources, enabling low-latency and high-throughput operation.
However, this architecture also places SRAM under extreme usage conditions, with long-duration, high-frequency, and repetitive access patterns. Failure modes such as read disturb, coupling interference, weak writes, sensing-margin instability, and retention-related data loss are more easily amplified in NPUs, becoming major contributors to yield loss and elevated DPPM. As a result, NPU performance bottlenecks often arise not from compute capability, but from the design and test quality of the memory subsystem.
This episode examines real NPU operating requirements to analyze how different SRAM usage scenarios—such as weight buffers, feature-map storage, accumulation buffers, and idle regions—introduce distinct reliability risks. It also explains why NPU SRAM testing cannot rely on a single, static test algorithm, and instead requires context-aware, configurable test and repair strategies to effectively mitigate mass-production risk.
To address these challenges, iSTART-TEK leverages the START™ v5 platform, combining the graphical and modular design of UDA (User-Defined Algorithms) to allow engineers to flexibly construct MBIST test flows tailored to different SRAM structures and access behaviors. In parallel, MART (MBIST Algorithm Recommendation Tool) systematizes accumulated SRAM test knowledge, enabling rapid selection of suitable algorithm combinations under multiple constraints, reducing decision cost and preventing production risks caused by improper test strategies.
Through the integrated use of UDA and MART, SRAM testing evolves beyond a basic pass/fail process, aligning more closely with real NPU AI workloads while simultaneously improving yield, controlling DPPM, and ensuring mass-production efficiency and product competitiveness.