Semiconductor Process Simulation Models for Faster ML Analysis
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Solution Overview
Problem
Machine learning for semiconductor device manufacturing requires extensive data and time, making it costly and inefficient for real-time process analysis.
Innovation Solution
An analysis method involving a system with a simulation server, data acquisition server, and estimation server to generate learning data and models quickly, using simulated process data to analyze and optimize semiconductor device manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to analyze semiconductor device manufacturing processes, then analysis accuracy is improved, but data collection time and cost increase enormously
Solution Approach 1:
The patent creates virtual copies of the manufacturing process through simulation. A simulation model replicates the semiconductor device manufacturing process, generating synthetic process data that mirrors real-world scenarios without requiring actual production runs. This copying approach enables machine learning model training using simulated data, dramatically reducing the time and cost of data collection while maintaining analysis accuracy.
Solution Approach 2:
The patent performs preliminary data generation through simulation before actual manufacturing. By pre-generating process data through simulation models, the system prepares training datasets in advance without waiting for real production data accumulation. This preliminary action enables immediate machine learning model development and process analysis, eliminating the time delay associated with collecting sufficient real-world data.
2Measurement precision
If machine learning is used to analyze semiconductor device manufacturing processes, then analysis accuracy is improved, but cost increases enormously
Solution Approach 1:
The patent creates virtual copies of the manufacturing process through simulation. A simulation model replicates the semiconductor device manufacturing process, generating synthetic process data that mirrors real-world scenarios without requiring actual production runs. This copying approach enables machine learning model training using simulated data, dramatically reducing the time and cost of data collection while maintaining analysis accuracy.
Solution Approach 2:
The patent uses inexpensive simulated data instead of expensive real production data. The simulation generates abundant process data at minimal cost, replacing the need for costly actual manufacturing runs solely for data collection. This substitution of cheap simulated data for expensive real data enables affordable machine learning implementation while achieving the same analytical objectives.
3Measurement precision
If extensive real process data is collected for machine learning, then model accuracy is improved, but processing time increases
Solution Approach 1:
The patent creates virtual copies of the manufacturing process through simulation. A simulation model replicates the semiconductor device manufacturing process, generating synthetic process data that mirrors real-world scenarios without requiring actual production runs. This copying approach enables machine learning model training using simulated data, dramatically reducing the time and cost of data collection while maintaining analysis accuracy.
Solution Approach 2:
The patent performs preliminary data generation through simulation before actual manufacturing. By pre-generating process data through simulation models, the system prepares training datasets in advance without waiting for real production data accumulation. This preliminary action enables immediate machine learning model development and process analysis, eliminating the time delay associated with collecting sufficient real-world data.
Data Source
AI summary
An analysis method of analyzing a process for manufacturing a semiconductor device, includes a preparing step of preparing a plurality of data sets each including an input to a simulator that simulates the process and an output from the simulator, a generating step of generating, based on the plurality of data sets, a plurality of learning data having, as a value of an explanatory variable, a value of information, of process information associated with at least one of control and a state of the process, to which attention is to be paid and a value of evaluation information for evaluating the process as a valued of an objective variable, and a learning step of generating a model expressing the process by performing learning based on the plurality of learning data generated in the generating step.