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Industrial Technology Research Institute


Intelligent Collaborative Process Analysis and Parameter Optimization Technique

Technology Overview

Intelligent Collaborative Process Analysis and Parameter Optimization Technique.
Intelligent Collaborative Process Analysis and Parameter Optimization Technique.

Process Analysis and Optimization (PAO) technique and AI virtual/process engineer collaborating system have the ability to quickly predict the product quality characteristics, and precisely recommend process parameters, to accelerate R&D speed and optimize process performance.

Applications & Benefits

With the rapid development of manufacturing technology in the world, how to improve the production yield and reduce production costs has become a key indicator for enterprises to improve competitiveness. However, as the process technology continues to keep pushing the limits, traditional process analysis techniques have been encountered bottlenecks. ITRI's Process analysis and optimization technique based on artificial intelligence, integrates a variety of advanced machine learning models to construct process models to describe the relationship between process parameters and product quality; research and develop advanced sequential sampling optimization algorithms to optimize product quality and production efficiency. The ultimate goal is to develop a complete set of process analysis and optimization methods to effectively shorten the development cycle, increase yield, and achieve time-to-market for advanced processes.

Optoelectronics, memory, chemical, steel, mechanical processing, biomedical, and other manufacturing industries. The technology has abilities of quickly predicting product qualities and suggesting process parameters, to increase development speed, reduce cost, and accelerate the manufacturer’s time to market.

Process analysis and optimization technique, combined with historical data and manufacturing field data, high-efficiency search algorithms, human-computer interaction mechanism, enable AI virtual engineers to have the ability to evaluate product quality, and provide process engineers parameter recommendations, and automatically update the model based on experimental results.

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