The nonstop pace of semiconductor innovation requires new approaches to manufacturing and stricter quality controls throughout the supply chain. With high-NA EUV setting the stage for chips with higher transistor counts, corresponding advancements in components and materials is vital. Tignis enables key suppliers—from manufacturers of photomasks and blanks, to photo resist chemicals, and wafer substrates—to achieve better manufacturing process automation, control and predictability.
The Tignis PAICe Maker and PAICe Monitor product suite is essential for components and materials manufacturers—from new product development to scaling and production. The improved outcomes are substantial.
PAICe Maker is an embedded AI controller that empowers your current and next-generation manufacturing lines to automatically make sophisticated and continuous optimization adjustments—significantly reducing process variance. PAICe Maker enables you to dramatically advance your ability to keep up with increasingly complex industry demands and seamlessly deliver the highest-quality components and materials to your customers.
PAICe Monitor takes in all the process-related data that you are collecting today, and automatically uncovers complex interactions between measured process variables and selected target variables. With the power of machine learning and physics, PAICe Monitor actively learns the nuances of your equipment, configurations and processes in order to autonomously detect process variations that lead to deviation in product quality. Rather than just alerting you to issues, Tignis’ software tells you why there’s a problem and what to do about it.
Custom AI, ML and physics models are notoriously difficult to develop. PAICe Monitor uses Tignis’ patented low code Digital Twin Query Language to put the advanced power of machine learning in the hands of your engineers and data scientists. It features an easy-to-use editor enabling your team to create and modify analytics for their custom processes. This expedites the path of identifying complex root causes and converting them into ongoing analytics solutions, facilitating a more robust and stable manufacturing process.