AI Predictive Modeling Tools Move Into Critical Role To Leverage AI, ML

The semiconductor industry, as always, is at the forefront of transformational technological innovation, driving escalating complexity of manufacturing processes that extend time-to-market delivery, inflate research and development costs, and lead to expensive delays when problems arise.

As manufacturers navigate these challenges, the need for comprehensive prediction of manufacturing problems and negative yield effects becomes increasingly critical. Companies are investing in artificial intelligence (AI) and machine learning (ML) skills with the goal of developing new models focused on these manufacturing prediction challenges. Many quickly realize that to impact production yield and manufacturing efficiency, these ML models need to be developed with consideration for the specific characteristics of semiconductor manufacturing data. They also need to be deployed where they would most impact process decisions.

The solution to this challenge is PDF Solutions’ ModelOps (Model Operations) for AI predictive modeling, which offers a structured approach to managing the lifecycle of machine learning models.

As the industry moves into the era of chiplets, digital twins, and lights-out manufacturing, large numbers of models are needed for a wide range of semiconductor smart manufacturing use cases. Flexible, modular systems are deployed for lifecycle management, monitoring, control, and operational implementation of AI, ML, or other types of decision models. Predictive modeling helps accelerate, scale, automate, and democratize the creation and deployment of models across the enterprise.

With ModelOps, users are able to apply third-party models or bring their own models and algorithms to the AI/ML model lifecycle management platform. They can predict test results based on fab data and/or upstream test point data, suggesting bin overrides at any step, detecting outlier or anomalous die, and classifying wafer map spatial fail patterns. They could predict maintenance events by detecting anomalous machine behavior and processes such as virtual metrology and classifying defect images. At the fabs and supply chains, they can maximize throughput, balancing supply and demand.

Models can be deployed offline or online at the edge to drive execution of real-time actions. Actions can vary from early identification of potential die failures to reduce test costs and sending automated alerts, to using ML for adaptive test/predictive burn-in to determine if a die is good enough to skip a test.

PDF Solutions’ ModelOps as an infrastructure solution for AI predictive modeling, anomaly detection, classification, and optimization in the semiconductor industry enables a structured approach to managing the lifecycle of machine learning models in the semiconductor manufacturing flow. It facilitates the transition from proof-of-concept models to scalable, production-ready solutions, enabling organizations to efficiently deploy AI models across various applications, such as predictive maintenance and yield optimization. Results of these models drive real-world actions across the manufacturing spectrum.

By enabling scalability, enhancing decision-making, and providing tailored solutions for complex environments, the PDF Solutions ModelOps solution not only addresses the unique challenges of semiconductor manufacturing but also drives significant business value. Its value lies in its ability to enhance decision-making, increase operational agility, and foster collaboration among teams. Integrating AI predictive modeling into manufacturing processes allows for improved predictions of equipment failures and optimized production schedules, ultimately propelling better yield and profitability.

As the industry continues to evolve, the adoption of ModelOps will be critical for organizations seeking to leverage AI effectively and maintain their competitive edge in a fast-paced market.

ModelOps will be demonstrated during the AI Executive Conference hosted by PDF Solutions Thursday, December 12, in San Francisco. The conference will showcase AI’s power to transform semiconductor design and manufacturing with examples of successful AI applications. Presentations will highlight how industry experts are deploying AI and ML to make a difference in their business.

PDF Solutions AI Executive Conference
Date: December 1‌2, 2‌0‌2‌4
Location: St. Regis Hotel, San Francisco, Calif.
Agenda and Registration