Author's Latest Posts

Reducing Chip Test Costs With AI-Based Pattern Optimization

The old adage “time is money” is highly applicable to the production testing of semiconductor devices. Every second that a wafer or chip is under test means that the next part cannot yet be tested. The slower the test throughput, the more automatic test equipment (ATE) is needed to meet production throughput demands. This is a huge issue for chip producers, since high pin counts, blazingly ... » read more

Power-Aware Test: Beyond Low-Power Test

By Rahul Singhal and Likith Kumar Manchukonda Power consumption is one of the key considerations when designing today’s semiconductor chips and systems. Over the years, the constant need for higher performance and more functions from the chips has been driving the continuous requirement for higher transistor density. The process node scaling makes this possible by reducing transistor sizes... » read more

Test Data Streaming For The Next Generation Of Designs

Semiconductor chips have been evolving to meet the demands of rapidly transforming applications, and so has the test technology to meet the test goals of those chips. Going back two decades or so, the applications were limited and the designs were simpler, thus the concerns about power, performance and area (PPA), turn-around time, re-use and time-to-market, etc., were important but not as crit... » read more

A Practical Approach To DFT For Large SoCs And AI Architectures, Part II

By Rahul Singhal and Giri Podichetty Part I of this article discusses the design-for-test (DFT) challenges of AI designs and strategies to address them at the die level. This part focuses on the test requirements of AI chips that integrate multiple dies and memories on the same package. Why 2.5D/3D chiplet-based designs for AI SoCs? Many semiconductor companies are adopting chiplet-based d... » read more

A Practical Approach To DFT For Large SoCs And AI Architectures, Part I

The traditional processors designed for general-purpose applications struggle to meet the computing demands and power budgets of artificial intelligence (AI) or machine leaning (ML) applications. Several semiconductor design companies are now developing dedicated AI/ML accelerators that are optimized for specific workloads such that they deliver much higher processing capabilities with much low... » read more

Don’t Let X Be A Problem For Logic BIST

By Rahul Singhal and Giri Podichetty A failure in the operation of integrated circuits (ICs) or chips deployed in safety-critical applications such as automotive, medical, and aerospace could have catastrophic consequences. These failures could stem from defects in the chip that escaped manufacturing tests or from transient faults that can occur during system operation due to factors such as... » read more

Power-Aware Test: Addressing Power Challenges In DFT And Test

Integrated circuit (IC) sizes continue to grow as they meet the compute requirements of cutting-edge applications such as artificial intelligence (AI), autonomous driving, and data centers. As design sizes increase, the total power consumption of the chip also increases. While process node scaling reduces a transistor’s size and its operating-voltage, power scaling has not kept up with the si... » read more

AI Testing: Pushing Beyond DFT Architectures

Every day, more applications are deploying artificial intelligence (AI) system to increase automation beyond traditional systems. The continuous growth in computing demands of AI systems require designers to develop massive, highly parallel AI processor chips. Their large sizes and types of applications have a significant impact on their design and test methodologies. With thousands of repeated... » 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