PAICe Monitor discovers sources of variance that are too complex for standard SPC charts and associated linear methods. With PAICe Monitor, you have the power to immediately identify complex and non-linear multivariate relationships between process variables and target metrics, automatically generate analytics to monitor for complex process deviations, and prevent future occurrences.
Tignis’ PAICe Monitor software application delivers AI and machine learning-enabled analytics for all stages of the semiconductor fabrication process lifecycle—from process development and ramp readiness, to high volume production. PAICe Monitor enables your process engineers to transform in-product fault diagnoses into continuous real-time monitoring—greatly improving time to diagnose, alerting to problems and predicting future faults.
Understanding the physics of your equipment lets you know how it should perform and how it will perform. While AI and ML can do similar things, they can only learn from data and recreate what has been previously observed. Applying physics enables you to achieve the same goals with significantly less data. Physics allows you to simulate modes that haven’t been observed before, select the best states of operation, and continually optimize processes.
Tignis' PAICe Monitor enables you to easily harness the power of AI, ML and physics-driven computational modeling throughout your fabrication processes by:
Tignis operates on the foundation that full transparency is key, every step of the way. With PAICe Monitor your process engineers can always see quantifiable data sets indicating the root cause of process variations that are causing product quality deviation in any manufacturing run or batch. PAICe Monitor clearly indicates which input parameters are causing problems, and unambiguously defines what your team should do to fix them.