AI Driven Digitalization Solution

Industry Challenges

The advancements in informatization and digitalization have significantly propelled the manufacturing industry forward. However, compared to the limitless potential of artificial intelligence, manufacturing customers still face considerable challenges.

Lack of data quality control leading to lack of model quality control

Poor data quality and inadequate training labels can lead to an increase in false alarms and missed detections, undermining confidence in digital systems.

Lack of robustness and capability to adapt to recipe and work regime changes 

Traditional models, built on single-variable statistics and rule engines, deteriorate quickly in dynamic environments, making them difficult to maintain and less effective over time.

Lack of talents

The scarcity of skilled professionals in AI and data science is stifling innovation and growth in the sector, especially on the plant floor, where combining data science with process expertise is critical.

Gaps between models for production performance and tool health

“Small” models focusing on metrology and tool health are independent from each other, while data and know-how from processes and quality are highly coupled.

Our Solution

We combine industrial large knowledge model with proprietary model, integrating operational, design, and IT technologies to unlock the value of industrial data for worry-free productions.

Consistent model performance through full-process data goodness control

Model performance is consistently optimized by monitoring and enhancing data goodness, which considers not only data integrity but also its impact on model outcomes. This is achieved through data quality inspection, pre-processing, and feature enhancement algorithms that cover the entire digital thread—from data acquisition to model results.

Adaptive models with hybrid feature engineering and online learning

Models leverage a combination of physics-inspired features, trace pattern analysis, and deep learning-based automated features to predict in a recipe-and-regime-aware manner. Model degradation is continuously monitored and improved through online learning, with performance validated against user-defined benchmarks.

AI Process Engineer offering know-how and data insights

The AI Process Engineer provides validated manufacturing insights into processes and tool health via intuitive, user-friendly chatbots. These are powered by a large industrial knowledge model and data insights derived from proprietary and transferable pretrained vertical models.

Production performance-oriented tool health and process optimization

Maximize yield and optimize performance by integrating "small" domain models with process know-how, providing synthesized insights from industrial-scale knowledge models through seamless integration and collaborative analysis.

Customer Value

Improve quality, increase efficiency, reduce cost, reduce storage and reduce pollution; Up to 12% increase in model accuracy, 10% in extension of key component life.