A Blueprint for Semiconductor Manufacturing Success with AI/ML Adoption

Process Optimization: Machine learning models excel at automating and optimizing semiconductor manufacturing processes. By analyzing process and equipment data, ML algorithms can identify subtle and complex issues that traditional methods cannot recognize. The rapid troubleshooting and root-cause-analysis capabilities of AI/ML solutions enable process engineers to proactively address manufacturing issues, driving improved product throughput and lower cost of goods sold (COGS).  This is especially beneficial in periods of high semiconductor demand when greater equipment availability has a direct and positive impact on product revenue.

Intelligent Maintenance: Equipment downtime is a necessary evil in semiconductor manufacturing. Tools need to be well-maintained in order for them to properly manufacture devices that inherently have tight manufacturing tolerances. Performing too much maintenance is expensive and reduces equipment availability. Insufficient maintenance (e.g. run to fail) can be equally expensive if it results in “long term down” situations in addition to potentially scrapping the wafers in process when a tool goes down. AI/ML delivers intelligent maintenance, identifying equipment failures before they occur, enabling equipment engineers to leverage scheduled downtime to include additional maintenance tasks that can eliminate unplanned and costly maintenance activities.

Equipment Inventory Management: Legacy semiconductor manufacturers have additional challenges because many of the tools used in their fabs can be decades old. Maintaining a proper supply of critical replacement parts is essential to maintain high-volume manufacturing, especially for parts with limited availability. Even for companies that choose “run to fail” manufacturing practices, AI/ML can provide clear visibility into the need for future replacement parts, enabling equipment support teams to be ready to address any equipment down situation with minimal impact on production throughput.  

Quality Control: In any manufacturing environment, process drift has always been a challenge for process engineers. Even the best designed processes will experience drift over time resulting in process variability than can affect quality.  AI/ML models are being used today to implement “run to run” process control that can actively manage process drift. Not only can process variation be reduced by 50% or more, chamber availability can also be increased by 1-3% providing additional capacity from the same tool fleet.

Cost Reduction: One of the most significant impacts of AI and ML in semiconductor manufacturing is overall cost reduction. Every one of the aforementioned areas has a direct impact on cost reduction: Engineering efficiency, smarter maintenance and inventory management, and improved product quality all contribute to lower COGS for semiconductor manufacturers.

Challenges and Future Directions: Despite the clear advantages, the integration of AI/ML into existing semiconductor manufacturing processes is not without challenges. High-quality data is the lifeblood of AI, and ensuring the timely availability of high-fidelity equipment and process data is essential to the success of any AI/ML implementation. Process engineering and equipment automation teams also need to be open to change, because AI/ML is much more than just “doing the same thing, but faster”. AI/ML is already changing the manufacturing paradigm that has existed for decades, and will continue to do so. Those that embrace change will be the winners in the next era of semiconductor manufacturing.

The future of AI/ML is bright. As these technologies continue to advance, they will continue to unlock untapped potential within the semiconductor industry. In fact, other than fab personnel, AI/ML is the only appreciating asset in your fab.  We can expect to see further improvements in efficiency, reductions in production costs, and the development of even more sophisticated chips primarily due to the benefits of AI/ML.

For Tignis, these advancements are particularly pertinent. Tignis is well-positioned to help semiconductor manufacturers and equipment OEMs to achieve new levels of automation and process control, ensuring that these companies remain at the leading edge of innovation.

AI/ML is not just supporting the semiconductor industry, it is reshaping it. As an industry, we are on the cusp of a new era, with AI/ML acting as the catalysts for innovation.

Sources:

McKinsey & Company. (2024, March 13). Insights on Artificial Intelligence. https://www.mckinsey.com/capabilities/quantumblack/our-insights

McKinsey & Company. (2021, April 2). Scaling AI in the sector that enables it: Lessons for semiconductor-device makers. https://www.mckinsey.com/industries/semiconductors/our-insights/scaling-ai-in-the-sector-that-enables-it-lessons-for-semiconductor-device-makers

Intel Newsroom. (2024, February 21). Intel Launches World’s First Systems Foundry Designed for the AI Era. https://www.intel.com/content/www/us/en/newsroom/news/foundry-news-roadmaps-updates.html

Intel Newsroom. (2023, November 3). Intel Labs Introduces AI Diffusion Model, Generates 360-Degree Images. https://www.intel.com/content/www/us/en/newsroom/news/intel-introduces-3d-generative-ai-model.html

TechOvedas. (2023, August 7). How AI is Revolutionizing the Semiconductor Industry. https://techovedas.com/how-ai-is-revolutionizing-the-semiconductor-industry/