AI: Great, But Somehow Still Not Very Good


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

Chip Industry Week In Review


Rapidus and IBM are jointly developing mass production capabilities for chiplet-based advanced packages. The collaboration builds on an existing agreement to develop 2nm process technology. Vanguard and NXP will jointly establish VisionPower Semiconductor Manufacturing Company (VSMC) in Singapore to build a $7.8 billion, 12-inch wafer plant. This is part of a global supply chain shift “Out... » read more

Navigating The Talent Crunch: AI Solutions For A Thriving Semiconductor Manufacturing Sector


The CHIPS and Science Act is a historic piece of legislation passed by the US government in 2022 aimed at regaining American leadership in semiconductor manufacturing. Supported by an unprecedented $52 billion in federal funding, this investment will also address supply chain vulnerabilities and national security concerns that were made glaringly public by the COVID epidemic. In addition to ... » read more

Using Predictive Maintenance To Boost IC Manufacturing Efficiency


Predicting exactly how and when a process tool is going to fail is a complex task, but it's getting a tad easier with the rollout of smart sensors, standard interfaces, and advanced data analytics. The potential benefits of predictive maintenance are enormous. Higher tool uptime correlates with greater fab efficiency and lower operating costs, so engineers are pursuing multiple routes to boo... » read more

Predicting And Preventing Process Drift


Increasingly tight tolerances and rigorous demands for quality are forcing chipmakers and equipment manufacturers to ferret out minor process variances, which can create significant anomalies in device behavior and render a device non-functional. In the past, many of these variances were ignored. But for a growing number of applications, that's no longer possible. Even minor fluctuations in ... » read more

AI Takes Aim At Chip Industry Workforce Training


When all the planned fabs become operational, the semiconductor industry is likely to face a worker shortage of 100,000 each in the U.S. and Europe, and more than 200,000 in Asia-Pacific, according to a McKinsey report. Since the dawn of technology, people have worried that robots, automation, and AI will steal their jobs, but these tools also can be put to use to help fill the chip industry ta... » read more

Digital Twins Target IC Tool And Fab Efficiency


Digital twins have emerged as the hot "new" semiconductor manufacturing technology, enabling fabs to create a virtual representation of a physical system on which to experiment and optimize what's going on inside the real fab. While digital twin technology has been in use for some time in other industries, its use has been limited in semiconductor manufacturing. What's changing is the breadt... » read more

Tackling Variability With AI-based Process Control


Jon Herlocker, co-founder and CEO of Tignis, sat down with Semiconductor Engineering to talk about how AI in advanced process control reduces equipment variability and corrects for process drift. What follows are excerpts of that conversation. SE: How is AI being used in semiconductor manufacturing and what will the impact be? Herlocker: AI is going to create a completely different factor... » read more

Pressure Builds On Failure Analysis Labs


Failure analysis labs are becoming more fab-like, offering higher accuracy in locating failures and accelerating time-to-market of new devices. These labs historically have been used for deconstructing devices that failed during field use, known as return material authorizations (RMAs), but their role is expanding. They now are becoming instrumental in achieving first silicon and ramping yie... » read more

Fabs Begin Ramping Up Machine Learning


Fabs are beginning to deploy machine learning models to drill deep into complex processes, leveraging both vast compute power and significant advances in ML. All of this is necessary as dimensions shrink and complexity increases with new materials and structures, processes, and packaging options, and as demand for reliability increases. Building robust models requires training the algorithms... » read more

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