Hierarchical DFT On A Flat Layout Design


The use of hierarchical DFT methods is growing as design size and complexity stresses memory requirements and design schedules.  Hierarchical DFT divides the design into smaller pieces, creates test structures and patterns at the core level, then retargets the core patterns to the chip level. But, if you need to perform the physical place and route on the full flat design, can you still take a... » read more

Challenges Of Logic BiST In Automotive ICs


The electronics in passenger cars continues to grow, and much of it is bound by the strict functional safety requirements formalized in the ISO 26262 standard. The ICs that drive the electronics systems in automobiles are also increasingly complex, designed to execute artificial intelligence algorithms that govern emerging self-driving capabilities. Designers are quickly adopting comprehensi... » read more

Smart Plug-And-Play DFT For Arm Cores


Modern SoCs are experiencing continued growth in capabilities and design sizes with more and more subsystem IPs being implemented. These large, complex, multi-core SoCs need strategies for DFT and ATPG that effectively reduce DFT effort, minimize ATPG runtime, and still achieve the target test coverage. Hierarchical DFT enables designing and testing of these designs in a systematic and repeatab... » read more

Breakthrough For Scan Diagnosis With Machine Learning


Cell-aware diagnosis is a new and effective way to detect defects inside standard cells. Industry standard failure analysis (FA) results from a major foundry show that cell-aware diagnosis is very effective at increasing the resolution of the diagnosis by reducing the number of suspects in cell-internal defect data. With advanced technology nodes, we have more complex layout structures and f... » read more

How To Manage DFT For AI Chips


Semiconductor companies are racing to develop AI-specific chips to meet the rapidly growing compute requirements for artificial intelligence (AI) systems. AI chips from companies like Graphcore and Mythic are ASICs based on the novel, massively parallel architectures that maximize data processing capabilities for AI workloads. Others, like Intel, Nvidia, and AMD, are optimizing existing archite... » read more