Unknowns Driving Up The Cost Of Auto IC Reliability


Automotive chipmakers are considering a variety of options to improve the reliability of ICs used for everything from sensors to artificial intelligence. But collectively they could boost the number of process steps, increase the time spent in manufacturing and packaging, and stir up concerns about the amount of data that needs to be collected, shared, and stored. Accounting for advanced pro... » read more

Week In Review: Manufacturing, Test


Government policy As reported, the United States is in dire need of more fab capacity as well as packaging plants. The U.S. took a big step in an effort to solve the problem. The U.S. House of Representatives this week introduced the America Competes Act of 2022. The bill includes funding for the Creating Helpful Incentives to Produce Semiconductors for America (CHIPS) Act, which is earmarked... » read more

Week In Review: Manufacturing, Test


Fabs Intel has announced plans for an initial investment of more than $20 billion in the construction of two new leading-edge fabs in Ohio. Planning for the first two factories will start immediately, with construction expected to begin late in 2022. Production is expected to come online in 2025. As part of the announcement, Air Products, Applied Materials, Lam Research and Ultra Clean Technol... » read more

The Gargantuan 5G Chip Challenge


Blazing fast upload and download speeds for cellular data are coming, but making the technology function as expected throughout its expected lifetime is an enormous challenge that will require substantial changes across the entire chip ecosystem. While sub-6GHz is an evolutionary step from 4G LTE, the real promise of 5G kicks in with millimeter-wave (mmWave) technology. But these higher-freq... » read more

Inspecting And Testing GaN Power Semis


As demand for new automotive battery electric vehicles (BEVs) heats up, automakers are looking for solutions to meet strict zero-defect goals in power semiconductors. Gallium nitride (GaN) and silicon carbide (SiC) wide-bandgap power semiconductors offer automakers a range of new EV solutions, but questions remain on how to meet the stringent quality goals of the automotive industry. Among t... » read more

Week In Review: Manufacturing, Test


Chipmakers TSMC has introduced another version of its 4nm process technology. The process, called N4X, is tailored for high-performance computing products. Recently, TSMC introduced another 4nm process, called N4P, which is an enhanced version of its 5nm technology. N4X is also an enhanced version of its 5nm technology. N4X, however, offers a performance boost of up to 15% over TSMC’s N5 pro... » read more

Using Manufacturing Data To Boost Reliability


As chipmakers turn to increasingly customized and complex heterogeneous designs to boost performance per watt, they also are demanding lower defectivity and higher yields to help offset the rising design and manufacturing costs. Solving those issues is a mammoth multi-vendor effort. There can be hundreds of process steps in fabs and packaging houses. And as feature sizes continue to shrink, ... » read more

Week In Review: Manufacturing, Test


Packaging and test Taiwan’s ASE--the world’s largest OSAT--has announced the proposed sale and disposal of equity interests in its subsidiaries, GAPT Holding and ASE (Kun Shan), to Wise Road Capital, a private equity firm based in China. The deal has a value of $1.46 billion. The announcement is related to four ASE assembly and test facilities in China, including Shanghai, Suzhou, Kunsh... » read more

Week In Review: Manufacturing, Test


Packaging Amkor plans to build a packaging plant in Bac Ninh, Vietnam. The first phase of the new factory will focus on providing system-in-package (SiP) assembly and test services for customers. The investment for the first phase of the facility is estimated to be between $200 million and $250 million. “This is a strategic, long-term investment in geographical diversification and factory... » read more

Easier And Faster Ways To Train AI


Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

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