tag: machine learning

IC Equipment Communication Standards Struggle As Data Volumes Grow
By Anne Meixner - 11 Feb, 2025 - Comments: 0

The tsunami of data produced during wafer fabrication cannot be effectively leveraged without standards. They determine how data is accessed from equipment, which users need data access and when, and how fast it can be delivered. On top of that, best practices in data governance and data quality are needed to effectively interpret collected data and transfer results. When fab automation and ... » read more

Using AI In Semiconductor Inspection
By Ed Sperling - 05 Feb, 2025 - Comments: 0

AI is exceptionally good at spotting anomalies in semiconductor inspection. The challenge is training different models for different inspection tools and topographies, and knowing which model to use at any particular time. Different textures in backgrounds are difficult for traditional algorithms, for example. But once machine learning models are trained properly, they have proven effective in ... » read more

AI In Data Management Has Limits
By Adam Kovac - 03 Feb, 2025 - Comments: 0

AI algorithms are being integrated into a growing number of EDA tools to automate different aspects of data management, but they also are forcing discussions about just how much decision-making should be turned over to machines and when that should happen. The ability of AI to sort through enormous amounts of design data to find patterns, both good and bad, is well recognized at this point. ... » read more

AI Won’t Replace Subject Matter Experts
By Adam Kovac - 15 Jan, 2025 - Comments: 0

Experts at The Table: The emergence of LLMs and other forms of AI has sent ripples through a number of industries, raising fears that many jobs could be on the chopping block, to be replaced by automation. Whether that’s the case in semiconductors, where machine learning has become an integral part of the design process, remains to be seen. Semiconductor Engineering sat down with a panel of e... » read more

Using Test And Metrology Data For Dynamic Process Control
By Gregory Haley - 14 Jan, 2025 - Comments: 0

Advanced packaging is transforming semiconductor manufacturing into a multi-dimensional challenge, blending 2D front-end wafer fabrication with 2.5D/3D assemblies, high-frequency device characterization, and complex yield optimization strategies. These combinations are essential to improving performance and functionality, but they create some thorny issues for which there are no easy fixes. ... » read more

How AI Is Transforming System Design
By Adam Kovac - 26 Nov, 2024 - Comments: 0

Experts At The Table: ChatGPT and other LLMs have attracted most of the attention in recent years, but other forms of AI have long been incorporated into design workflows. The technology has become so common that many designers may not even realize it’s a part of the tools they use every day. But its adoption is spreading deeper into tools and methodologies. Semiconductor Engineering sat down... » read more

To (B)atch Or Not To (B)atch?
By Steve Roddy - 18 Nov, 2024 - Comments: 0

When evaluating benchmark results for AI/ML processing solutions, it is very helpful to remember Shakespeare’s Hamlet, and the famous line: “To be, or not to be.” Except in this case the “B” stands for Batched. Batch size matters There are two different ways in which a machine learning inference workload can be used in a system. A particular ML graph can be used one time, preced... » read more

LLMs Show Promise In Secure IC Design
By Adam Kovac - 21 Oct, 2024 - Comments: 0

The introduction of large language models into the EDA flow could significantly reduce the time, effort, and cost of designing secure chips and systems, but they also could open the door to more sophisticated attacks. It's still early days for the use of LLMs in chip and system design. The technology is just beginning to be implemented, and there are numerous technical challenges that must b... » read more

Paving The Way For Sustainable AI
By Infineon - 10 Oct, 2024 - Comments: 0

Real-time requirements and the need for power-efficiency, security and privacy drives AI-processing at the edge. Key benefits of Edge AI include: -Low latency and real-time response -High power efficiency -Improved security and data privacy -Reduced cost A complementary set of AI-specific products and solutions, an end-to-end ML platform as well as an extensive application kno... » read more

The Cost Of EDA Data Storage And Processing Efficiency
By Adam Kovac - 09 Oct, 2024 - Comments: 0

Engineering teams are turning to the cloud to process and store increasing amounts of EDA data, but while the compute resources in hyperscale data centers are virtually unlimited, the move can add costs, slow access to data, and raise new concerns about sustainability. For complex chip designs, the elasticity of the cloud is a huge bonus. With advanced-node chips and packaging, the amount of... » read more

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