New MEMS-based products are constantly emerging, fueled by the Internet of Things (IoT), autonomous driving, smart manufacturing and healthcare applications. The MEMS pressure sensor market is no exception to this trend1. Its growth has been driven mainly by automotive applications such as tire pressure management system (TPMS) regulations in China, fuel and ignition systems, thermal systems, oil-pressure monitoring, and indoor and outdoor navigation systems. Easy to customize and integrate, miniature, sensitive, accurate and low-power MEMS devices are especially well-suited to the accuracy, power consumption, sensitivity and miniaturization that pressure sensors require.
By Ed Sperling and Mark LaPedus
Packaging is emerging as one of the most critical elements in semiconductor design, but it’s also proving difficult to master both technically and economically.
The original role of packaging was simply to protect the chips inside, and there are still packages that do just that. But at advanced nodes, and with the integration of heterogeneous components built using different manufacturing processes, packaging is taking on a much broader and more strategic role. Many of the new packages are application-specific, and they are an integral part of the system architecture. They can help channel heat, improve performance, help to reduce power, and even safeguard signal integrity.
Chipmakers are using more and different traditional tool types than ever to find killer defects in advanced chips, but they are also turning to complementary solutions like advanced forms of machine learning to help solve the problem
By ED SPERLING
Semiconductor equipment vendors are starting to add more sensors into their tools in an effort to improve fab uptime and wafer yield, and to reduce cost of ownership and chip failures.
Massive amounts of data gleaned from those tools is expected to provide far more detail than in the past about multiple types and sources of variation, including when and where that variation occurred and how, when and why equipment failures occur. Combined with data about device failures in the field, along with such things as design layout and verification, it’s becoming possible to create a detailed timeline of how chips were designed, manufactured and what goes wrong along the way. That, in turn, can be used to improve quality, identify potential sources of defects, and add increased efficiency into processes.
Variation is becoming more problematic as chips become increasingly heterogeneous and as they are used in new applications and different locations, sparking concerns about how to solve these issues and the full impact will be.
By Mark Lapedus
By Ed Sperling
By Ed Sperling
David Fried, CTO at Coventor, a Lam Research Company, sat down with Semiconductor Engineering to talk about how AI and Big Data techniques will be used to improve yield and quality in chip manufacturing. What follows are excerpts of that conversation.
SE: We used to think about manufacturing data in terms of outliers, but as tolerances become tighter at each new node that data may need to be examined even within what is considered the normal range. What’s the impact of that on manufacturing?
Fried: When I started in CMOS at 200mm, there was some data on the tools in the fab, but by and large, we were losing it as soon as it was created. When we went to 300mm, we got better at putting sensors on tools, generating data, and in some cases looking at it.