Semiconductor Retrieval Apparatus Using Prediction Model Optimization
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Solution Overview
Problem
Current semiconductor treatment apparatuses face challenges in efficiently determining optimal input parameters due to the vast and complex apparatus parameter space, leading to lengthy retrieval times and a low probability of reaching the best solution, especially with nonlinear input-output relationships and numerous local solutions.
Innovation Solution
A retrieval apparatus that uses a processor and memory to generate a prediction model based on input and output data, performs demonstration tests, and updates the model to efficiently optimize the operation of the semiconductor treatment apparatus by narrowing the retrieval region and avoiding local solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a wide control range and multiple control parameters are used to improve semiconductor device performance and productivity, then the ability to produce high-performance devices increases, but the difficulty of determining optimal parameter combinations and the time required for development increase significantly
Solution Approach 1:
The patent implements an automated feedback system where sensor data and monitor data from the semiconductor treatment apparatus are continuously collected, analyzed, and used to automatically determine optimal control parameters. The system compares actual processing results with target values and automatically adjusts parameters, eliminating the need for manual trial-and-error optimization by engineers.
Solution Approach 2:
The semiconductor treatment apparatus is equipped with autonomous capabilities to self-diagnose and self-optimize its processing conditions. The built-in analysis system automatically processes sensor and monitor data to determine optimal control parameters without external intervention, allowing the apparatus to maintain and improve its own performance automatically.
2Measurement precision
If multiple sensors and monitors are mounted to acquire comprehensive processing data, then the accuracy of processing control improves, but the amount of data to be analyzed increases, making control method development harder
Solution Approach 1:
The system performs preliminary data processing and analysis automatically as data is being collected from sensors and monitors. By pre-processing the data stream in real-time and identifying relevant patterns immediately, the system avoids the need for lengthy post-processing analysis, thus maintaining high measurement precision while minimizing time loss.
Solution Approach 2:
The patent replaces manual data analysis methods with automated computational analysis systems. Instead of engineers manually analyzing large volumes of sensor and monitor data, an automated algorithm processes the data rapidly, identifying optimal control parameters without the time constraints of human analysis while maintaining or improving accuracy.
3Manufacturing precision
If top engineers manually determine control parameters based on their knowledge and techniques, then the quality of processing control improves, but the insufficient number of top engineers becomes a bottleneck for handling increased number of processes
Solution Approach 1:
The system empowers the semiconductor treatment apparatus to automatically determine optimal control parameters without relying on external expert knowledge. The built-in analysis system processes sensor and monitor data to autonomously identify the best parameter combinations, allowing any operator to achieve expert-level control quality while enabling the handling of numerous processes simultaneously.
Solution Approach 2:
The patent implements dynamic parameter adjustment based on real-time analysis of processing data. Instead of relying on static expert knowledge, the system continuously adapts control parameters by analyzing actual sensor and monitor readings, allowing automatic optimization that matches or exceeds expert performance while scaling to handle increased process volumes.
Data Source
AI summary
A retrieval apparatus includes a processor and a memory and retrieves a condition given to a semiconductor treatment apparatus. The processor receives a processing result of a semiconductor, a condition corresponding to the processing result, a target value for treating the semiconductor, and a retrieval region. A prediction model is generated indicating a relationship between the condition and the processing result based on a set value of the condition in the retrieval region, and the processing result; calculates a predicted value, performs a demonstration test, acquires an actually measured value, outputs the predicted value as a set value when the actually measured value reaches the target value. When the actually measured value does not reach the target value, the prediction model is updated by applying the predicted value and the actually measured value to the set value and the processing result, respectively.