tag: ML

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

IC Industry’s Growing Role In Sustainability
By Ann Mutschler - 17 Jun, 2024 - Comments: 1

The massive power needs of AI systems are putting a spotlight on sustainability in the semiconductor ecosystem. The chip industry needs to be able to produce more efficient and lower-power semiconductors. But demands for increased processing speed are rising with the widespread use of large language models and the overall increase in the amount of data that needs to be processed. Gartner estima... » 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

Opportunities Grow For GPU Acceleration
By Gregory Haley - 05 Jun, 2024 - Comments: 0

Experts at the Table: Semiconductor Engineering sat down to discuss the impact of GPU acceleration on mask design and production and other process technologies, with Aki Fujimura, CEO of D2S; Youping Zhang, head of ASML Brion; Yalin Xiong, senior vice president and general manager of the BBP and reticle products division at KLA; and Kostas Adam, vice president of engineering at Synopsys. W... » read more

Chip Design Digs Deeper Into AI
By Ann Mutschler - 03 Jun, 2024 - Comments: 0

Growing demand for blazing fast and extremely dense multi-chiplet systems are pushing chip design deeper into AI, which increasingly is viewed as the best solution for sifting through scores of possible configurations, constraints, and variables in the least amount of time. This shift has broad implications for the future of chip design. In the past, collaborations typically involved the chi... » read more

Using Predictive Maintenance To Boost IC Manufacturing Efficiency
By Laura Peters - 14 May, 2024 - Comments: 0

Predicting exactly how and when a process tool is going to fail is a complex task, but it's getting a tad easier with the rollout of smart sensors, standard interfaces, and advanced data analytics. The potential benefits of predictive maintenance are enormous. Higher tool uptime correlates with greater fab efficiency and lower operating costs, so engineers are pursuing multiple routes to boo... » read more

Dealing With AI/ML Uncertainty
By Karen Heyman - 25 Apr, 2024 - Comments: 0

Despite their widespread popularity, large language models (LLMs) have several well-known design issues, the most notorious being hallucinations, in which an LLM tries to pass off its statistics-based concoctions as real-world facts. Hallucinations are examples of a fundamental, underlying issue with LLMs. The inner workings of LLMs, as well as other deep neural nets (DNNs), are only partly kno... » read more

Predicting And Preventing Process Drift
By Gregory Haley - 22 Apr, 2024 - Comments: 0

Increasingly tight tolerances and rigorous demands for quality are forcing chipmakers and equipment manufacturers to ferret out minor process variances, which can create significant anomalies in device behavior and render a device non-functional. In the past, many of these variances were ignored. But for a growing number of applications, that's no longer possible. Even minor fluctuations in ... » read more

Embrace The New!
By Steve Roddy - 14 Mar, 2024 - Comments: 0

The ResNet family of machine learning algorithms was introduced to the AI world in 2015. A slew of variations was rapidly discovered that at the time pushed the accuracy of ResNets close to the 80% threshold (78.57% Top 1 accuracy for ResNet-152 on ImageNet). This state-of-the-art performance at the time, coupled with the rather simple operator structure that was readily amenable to hardware ac... » read more

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