tag: ML

To (B)atch Or Not To (B)atch?
By Steve Roddy - 18 Nov, 2024 - Comments: 0

When evaluating benchmark results for AI/ML processing solutions, it is very helpful to remember Shakespeare’s Hamlet, and the famous line: “To be, or not to be.” Except in this case the “B” stands for Batched. Batch size matters There are two different ways in which a machine learning inference workload can be used in a system. A particular ML graph can be used one time, preced... » read more

AI’s Power To Transform Semiconductor Design And Manufacturing
By John Kibarian - 28 Oct, 2024 - Comments: 0

Artificial intelligence and machine learning (AI/ML) have immense power to transform semiconductor design and manufacturing for a variety of broad and far-ranging applications. Just consider the volume of data generated by design and manufacturing each year. With increasingly complex products, machines, processes and supply chains, the overall amount of data associated with semiconductor making... » read more

ML Model Usage For Various Life Stages Of Semiconductor Test
By Shinji Hioki - 15 Oct, 2024 - Comments: 0

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

Increasing Roles For Robotics In Fabs
By Gregory Haley - 19 Aug, 2024 - Comments: 0

Different types of robots with greater precision and mobility are beginning to be deployed in semiconductor manufacturing, where they are proving both reliable and cost-efficient. Static robots have been used for years inside of fabs, but they now are being supplemented by collaborative robots (cobots), autonomous mobile robots (AMRs), and autonomous humanoid robots to meet growing and widen... » read more

AI/ML’s Role In Design And Test Expands
By Laura Peters - 06 Aug, 2024 - Comments: 1

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

Digital Twins Find Their Footing In IC Manufacturing
By Laura Peters - 09 Jul, 2024 - Comments: 0

Momentum is building for digital twins in semiconductor manufacturing, tying together the various processes and steps to improve efficiency and quality, and to enable more flexibility in the fab and assembly house. The movement toward digital twins opens up a slew of opportunities, from building and equipping new fabs faster to speeding yield ramps by reducing the number of silicon-based tes... » read more

Navigating The Future Of EDA
By Anika Sunda - 27 Jun, 2024 - Comments: 0

The landscape of electronic design automation (EDA) is undergoing a monumental transformation. The catalysts? Artificial Intelligence (AI) and Machine Learning (ML). These technological marvels are not just reshaping how we approach design and verification in electronics; they are redefining the possibilities within the field. Our latest podcast episode delved deep into this topic, uncovering t... » read more

Applying Machine Learning To Accelerate TCAD Calibration
By Dwight Hunter - 20 Jun, 2024 - Comments: 0

TCAD models are the fundamental building blocks for the semiconductor industry. Whether it is a new process node or a new multi-billion dollar fab, accurate TCAD models must be developed and calibrated before they can be deployed in technology development. While TCAD models have been around for (many) decades, their complexity is growing exponentially, as is the demands placed on the R&D en... » read more

Precise Control Needed For Copper Plating And CMP
By Anne Meixner - 20 Jun, 2024 - Comments: 0

Chipmakers are relying on machine learning for electroplating and wafer cleaning at leading-edge process nodes, augmenting traditional fault detection/classification and statistical process control in order to extend the usefulness of copper interconnects. Copper is well understood and easy to work with, but it is running out of steam. At 5nm and below, copper plating tools are struggling to... » read more

AI: Great, But Somehow Still Not Very Good
By Charlie Parker - 20 Jun, 2024 - Comments: 0

In an invited presentation at CS Mantech 2024, Charlie Parker, senior machine learning engineer at Tignis, provides context for the AI hype cycle with a high-level overview of machine learning concepts, then explores how the technology fits into the fab, from inventory management to institutional knowledge capture, but warns that it is worth being aware of the ways in which machine learning mod... » read more

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