Tignis
Artificial Intelligence process control and analytics-based monitoring software
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Tignis® – proven ability to design and deploy deep technology-based enterprise Artificial Intelligence (AI) analytics software at scale. Their solutions include:
PAICe Monitor® an AI-driven anomaly detection and predictive maintenance platform, enabling customers to quickly deploy the power of Machine Learning for deep analysis and prediction models.
PAICe Maker® an AI-driven advanced process control solution for manufacturing that automatically adjusts for process drift over time.
Digital Twin Query Language (DTQL) provides syntax enabling users to manage complex data cleaning and analytics tasks without requiring data science experience.
PAICe Monitor
AI-driven anomaly detection and predictive maintenance platform.
- Easy-to-use ML-powered analytics and control solution ready for immediate deployment
- Accelerates time-to-insights for domain experts
How:
- Rapid signal discovery via ML-based correlation engine to predict tool failures, yield excursions, and defect issues
- Innovative method to visually identify and troubleshoot real-time, time-series features (e.g. sagging, overshoot, etc.)
PAICe Maker
AI-driven prescriptive process control solution for manufacturing.
- ML-driven process control which automatically adjusts for expected and unexpected process drift
- Reduces maintenance efforts related to drift correction
How:
- Real-time, run-to-run process control
- Patented process characterization to ensure optimal process adjustment
- Seamless integration with PAICe Monitor to maximize effective detection of process risks
Digital Twin Query Language
Provides syntax enabling users to manage complex data cleaning and analytics tasks without requiring data science experience.
- Built for processing massive amounts of raw time-series data, and fully integrated with Python & Jupyter Notebook to easily assimilate into most fab environments
- Enables users to manage complex data cleaning and analytics tasks without requiring data science experience
- Seamless analysis of process variability (e.g. exploration, testing, development, and deployment of predictive ML models)
