tag: machine learning

Leveraging Machine Learning in Semiconductor Yield Analysis
By DR Yield - 09 Jul, 2024 - Comments: 0

Searching through wafer maps looking for spatial patterns is not only a very time-consuming task to be done manually, it’s also prone to human oversight and error, and nearly impossible in a large fab where there are thousands of wafers a day being processed. We developed a tool that applies automatic spatial pattern detection algorithms using ML, parametrizing pattern recognition and clas... » read more

Applying Machine Learning To Accelerate TCAD Calibration
By Dwight Hunter - 20 Jun, 2024 - Comments: 0

TCAD models are the fundamental building blocks for the semiconductor industry. Whether it is a new process node or a new multi-billion dollar fab, accurate TCAD models must be developed and calibrated before they can be deployed in technology development. While TCAD models have been around for (many) decades, their complexity is growing exponentially, as is the demands placed on the R&D en... » read more

Precise Control Needed For Copper Plating And CMP
By Anne Meixner - 20 Jun, 2024 - Comments: 0

Chipmakers are relying on machine learning for electroplating and wafer cleaning at leading-edge process nodes, augmenting traditional fault detection/classification and statistical process control in order to extend the usefulness of copper interconnects. Copper is well understood and easy to work with, but it is running out of steam. At 5nm and below, copper plating tools are struggling to... » read more

AI: Great, But Somehow Still Not Very Good
By Charlie Parker - 20 Jun, 2024 - Comments: 0

In an invited presentation at CS Mantech 2024, Charlie Parker, senior machine learning engineer at Tignis, provides context for the AI hype cycle with a high-level overview of machine learning concepts, then explores how the technology fits into the fab, from inventory management to institutional knowledge capture, but warns that it is worth being aware of the ways in which machine learning mod... » read more

KAN: Kolmogorov Arnold Networks: An Alternative To MLPs (MIT, CalTech, et al.)
By Technical Paper Link - 13 Jun, 2024 - Comments: 0

A new technical paper titled "KAN: Kolmogorov-Arnold Networks" was published by researchers at MIT, CalTech, Northeastern University and The NSF Institute for Artificial Intelligence and Fundamental Interactions. Abstract: "Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While... » read more

KANs Explode!
By Steve Roddy - 13 Jun, 2024 - Comments: 0

In late April 2024, a novel AI research paper was published by researchers from MIT and CalTech proposing a fundamentally new approach to machine learning networks – the Kolmogorov Arnold Network – or KAN. In the six weeks since its publication, the AI research field is ablaze with excitement and speculation that KANs might be a breakthrough that dramatically alters the trajectory of AI mod... » read more

Why It’s So Hard To Secure AI Chips
By Adam Kovac - 06 Jun, 2024 - Comments: 0

Demand for high-performance chips designed specifically for AI applications is spiking, driven by massive interest in generative AI at the edge and in the data center, but the rapid growth in this sector also is raising concerns about the security of these devices and the data they process. Generative AI — whether it's OpenAI’s ChatGPT, Anthropic’s Claude, or xAI’s Grok — sifts thr... » read more

MCU Changes At The Edge
By Ed Sperling - 03 Jun, 2024 - Comments: 0

Microcontrollers are becoming a key platform for processing machine learning at the edge due to two significant changes. First, they now can include multiple cores, including some for high performance and others for low power, as well as other specialized processing elements such as neural network accelerators. Second, machine learning algorithms have been pruned to the point where inferencing ... » read more

AI For Data Management
By Adam Kovac - 30 May, 2024 - Comments: 0

Data management is becoming a significant new challenge for the chip industry, as well as a brand new opportunity, as the amount of data collected at every step of design through manufacturing continues to grow. Exacerbating the problem is the rising complexity of designs, many of which are highly customized and domain-specific at the leading edge, as well as increasing demands for reliabili... » read more

Hardware Fuzzer Utilizing LLMs
By Technical Paper Link - 12 Apr, 2024 - Comments: 0

A new technical paper titled "Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing" was published by researchers at TU Darmstadt and Texas A&M University. Abstract "Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware ... » read more

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