tag: AI

Pressure Builds On Failure Analysis Labs
By Laura Peters - 09 Jan, 2024 - Comments: 0

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

Unlocking Value: The Power of AI in Semiconductor Test
By Advantest - 09 Jan, 2024 - Comments: 0

AI (Artificial Intelligence) and data analytics empower semiconductor manufacturers to extract valuable insights from the massive amounts of data generated throughout the silicon lifecycle. By leveraging AI algorithms, semiconductor manufacturers can optimize silicon design, assembly, and testing processes. Through the analysis of vast datasets, AI can identify patterns, predict failures, and o... » read more

SRAM’s Role In Emerging Memories
By Karen Heyman - 05 Jan, 2024 - Comments: 1

Experts at the Table — Part 3: Semiconductor Engineering sat down to talk about AI, the latest issues in SRAM, and the potential impact of new types of memory, with Tony Chan Carusone, CTO at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be ... » read more

2023: A Good Year For Semiconductors
By Brian Bailey - 21 Dec, 2023 - Comments: 0

Looking back, 2023 has had more than its fair share of surprises, but who were the winners and losers? The good news is that by the end of the year, almost everyone was happy. That is not how we exited 2022, where there was overcapacity, inventories had built up in many parts of the industry, and few sectors — apart from data centers — were seeing much growth. The supposed new leaders we... » read more

Fabs Begin Ramping Up Machine Learning
By Anne Meixner - 19 Dec, 2023 - Comments: 0

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

Data Formats For Inference On The Edge
By Brian Bailey - 14 Dec, 2023 - Comments: 0

AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

Requirements For The Efficient Implementation Of AI Solutions On Edge Devices
By André Schneider - 14 Dec, 2023 - Comments: 0

By André Schneider, Olaf Enge-Rosenblatt, and Björn Zeugmann In recent years, there has been a growing tendency to implement data-driven approaches for the continuous monitoring of industrial plants as part of digitalization and Industry 4.0 initiatives. The hope is to detect critical conditions at an early stage, minimize maintenance and downtimes, and continuously achieve high product qu... » read more

The Uncertain Future Of In-Memory Compute
By Karen Heyman - 13 Dec, 2023 - Comments: 0

Experts at the Table — Part 2: Semiconductor Engineering sat down to talk about AI and the latest issues in SRAM with Tony Chan Carusone, chief technology officer at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be found here and part 3 is h... » read more

Testing ICs Faster, Sooner, And Better
By Laura Peters - 12 Dec, 2023 - Comments: 0

The infrastructure around semiconductor testing is changing as companies build systems capable of managing big data, utilizing real-time data streams and analysis to reduce escape rates on complex IC devices. At the heart of these tooling and operational changes is the need to solve infant mortality issues faster, and to catch latent failures before they become reliability problems in the fi... » read more

AI Races To The Edge
By Karen Heyman - 07 Dec, 2023 - Comments: 0

AI is becoming increasingly sophisticated and pervasive at the edge, pushing into new application areas and even taking on some of the algorithm training that has been done almost exclusively in large data centers using massive sets of data. There are several key changes behind this shift. The first involves new chip architectures that are focused on processing, moving, and storing data more... » read more

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