Extreme Quality Semiconductor Manufacturing, Part 1: Automotive


By Ben Tsai and Cathy Perry Sullivan Across the full range of semiconductor device types and design nodes, there is a drive to produce chips with significantly higher quality. Automotive, IoT and other industrial applications require chips that achieve very high reliability over a long period of time, and some of these chips must maintain reliable performance while operating in an environmen... » read more

What Engineers Are Reading And Watching


By Brian Bailey And Ed Sperling An important indicator of where the chip industry is heading is what engineers are reading and what videos they are watching. While some subjects remain on top, such as the level of interest in the latest manufacturing technologies, other areas come and go. The stories with the biggest traffic numbers are almost identical to last year. Readers want to know wh... » read more

5 Steps To Becoming A Data-Driven Manufacturer


What does it take to become a data-driven manufacturer. There are five fundamental steps: Get the data you need Organize the data Analyze and present the data Automate analytics Innovate Companies constantly strive to become data-driven. In this way, their decisions can become more objective and more likely to achieve the desired results. In fact, many companies understand t... » read more

Making Random Variation Less Random


The economics for random variation are changing, particularly at advanced nodes and in complex packaging schemes. Random variation always will exist in semiconductor manufacturing processes, but much of what is called random has a traceable root cause. The reason it is classified as random is that it is expensive to track down all of the various quirks in a complex manufacturing process or i... » read more

Who’s Watching The Supply Chain?


Every company developing chips at the most advanced process nodes these days is using different architectures and heterogeneous processing and memory elements. There simply is no other way to get the kind of power/performance improvements needed to justify the expense of moving to a new process node. So while they will reap the benefits of traditional scaling, that alone is no longer enough. ... » read more

Big Data Trends Shaping Industry 4.0


In today’s highly competitive economy, automotive OEMs are under pressure to shorten production cycles and fully leverage production capacities, with no compromising of quality and safety. This ebook covers the three big data mega trends for the automotive value chain: ML and AI to streamline processes Actionable Insights Optimized product reliability Don’t lag be... » read more

Recent Earthquakes Highlight Risk To Semiconductor Manufacturing Sites


On July 4, 2019, southern California experienced a 6.4 magnitude earthquake followed by a 7.1 earthquake the next day. Both earthquakes occurred near the town of Ridgecrest, but they were not related to the San Andreas fault, an 800-mile fault zone in California where two tectonic plates meet. The San Andreas fault is generally considered to be where “the big one” could occur in California,... » read more

SEMI Calls For U.S.-China Tariff Removals


In testimony today before a U.S. government interagency panel considering tariffs on $300 billion worth of Chinese goods, SEMI called for the removal of about 30 tariff lines, which cover items central to the semiconductor manufacturing process. Mike Russo, vice president of global industry advocacy at SEMI, explained in his testimony that while SEMI strongly supports efforts to better... » read more

Thinking Differently About IIoT Analytics


Manufacturers are rushing to keep up with the latest technology trends and perhaps the most significant ones are around the smart factory. Whether you call it Industry 4.0, Smart Manufacturing or the Industrial Internet of Things (IIoT), what all these initiatives have in common is the desire to maximize value from manufacturing data and improve overall manufacturing efficiency. With the ave... » read more

Using ML For Post-Silicon Validation


Ira Leventhal, vice president of Advantest’s new concept product initiative, talks about how to use machine learning to ferret out hidden relationships in a complex design and to utilize that data to improve chips. » read more

← Older posts