ML Model Usage For Various Life Stages Of Semiconductor Test


By Shinji Hioki and Ken Butler From development through high volume manufacturing (HVM), semiconductor manufacturers’ pain points change based on the life stages. Each stage requires different types of applications to help with business needs. At the early stage, where the design and process are still immature, understanding the root causes of maverick material and implementing fixes is th... » read more

Signals In The Noise: Tackling High-Frequency IC Test


The need for high-frequency semiconductor devices is surging, fueled by growing demand for advanced telecommunications, faster sensors, and increasingly autonomous vehicles. The advent of millimeter-wave communication in 5G and 6G is pushing manufacturers to develop chips capable of handling frequencies that were once considered out of reach. However, while these technologies promise faster ... » read more

Chip Industry Week In Review


Imec announced a new automotive chiplet consortium to evaluate which different architectures and packaging technologies are best for automotive applications. Initial members includes Arm, ASE, Cadence, Siemens, Synopsys, Bosch, BMW, Tenstorrent, Valeo, and SiliconAuto. Imec also launched star, a global network bringing together automotive and semiconductor innovators to address technological c... » read more

Using AI To Glue Disparate IC Ecosystem Data


AI holds the potential to change how companies interact throughout the global semiconductor ecosystem, gluing together different data types and processes that can be shared between companies that in the past had little or no direct connections. Chipmakers always have used abstraction layers to see the bigger picture of how the various components of a chip go together, allowing them to pinpoi... » read more

Promises and Perils of Parallel Test


Testing multiple devices at the same time is not providing the equivalent reduction in overall test time due to a combination of test execution issues, the complexity of the devices being tested, and the complex tradeoffs required for parallelism. Parallel testing is now the norm — from full wafer probe DRAM testing with thousands of dies to two-site testing for complex, high-performance c... » read more

Standardizing Defect Coverage In Analog/Mixed Signal Test


A newly drafted IEEE standard will bring more consistency to defect metrics in analog/mixed (AMS) designs, a long-overdue step that has become too difficult to ignore in the costly heterogeneous assemblies being deployed inside of data centers and mobile devices. Standardizing analog is no simple feat due to the legacy approach to AMS design, and this is not the first attempt at improving te... » read more

From Mobile Phones To Robotics: How The Industry Continues To Drive Innovation


I recently had the opportunity to host Pierre Cambou, Principal Analyst for Global Semiconductors at Yole Group, on the Advantest podcast. What struck me about our conversation was while we focused on what was going on in the mobile market, the entire talk was reflective of the cyclical nature of the semiconductor industry and how technology can drive intense cycles of innovation. As Pierre ... » read more

Chip Industry Week In Review


Three Fraunhofer Institutes (IIS/EAS, IZM, and ENAS) launched the Chiplet Center of Excellence, a research initiative to support the commercial introduction of chiplet technology. The center initially will focus on automotive electronics, developing workflows and methods for electronics design, demonstrator construction, and the evaluation of reliability. The UCIe Consortium published the Un... » read more

AI/ML’s Role In Design And Test Expands


The role of AI and ML in test keeps growing, providing significant time and money savings that often exceed initial expectations. But it doesn't work in all cases, sometimes even disrupting well-tested process flows with questionable return on investment. One of the big attractions of AI is its ability to apply analytics to large data sets that are otherwise limited by human capabilities. In... » read more

Leveraging AI To Efficiently Test AI Chips


In the fast-paced world of technology, where innovation and efficiency are paramount, integrating artificial intelligence (AI) and machine learning (ML) into the semiconductor testing ecosystem has become of critical importance due to ongoing challenges with accuracy and reliability. AI and ML algorithms are used to identify patterns and anomalies that might not be discovered by human testers o... » read more

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