Stepper Control Equipment Using Deep Learning for Precision Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The precision of stepper control in semiconductor manufacturing processes, particularly in exposure and etching processes, is compromised due to the increasing miniaturization of trace widths, requiring more accurate calibration of beam focal length and exposure energy to achieve high precision circuit formation.
Innovation Solution
The implementation of artificial intelligence technology using a depth learning algorithm, specifically an auto-encoder and generative adversarial network algorithm, to analyze sample and online development patterns, generating finer scales of focal length and energy values, enabling precise calibration of the stepper's beam focal length and exposure energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional calibration methods are used for stepper, then the operation is simple and easy to understand, but the manufacturing precision deteriorates due to trace width miniaturization
Solution Approach 1:
The patent replaces conventional mechanical/optical calibration methods with an artificial intelligence-based system that uses depth learning algorithms to analyze development patterns and automatically determine focal length and energy values, thereby achieving high precision without complex manual calibration procedures
Solution Approach 2:
The system enables self-service calibration by automatically analyzing development patterns through AI algorithms to determine calibration parameters without requiring skilled operators or complex calibration equipment, allowing the stepper to self-calibrate and maintain high precision
2Measurement precision
If manual calibration by operator is used, then the device complexity is low, but the measurement precision deteriorates due to increasing miniaturization requirements
Solution Approach 1:
The patent replaces manual optical measurement methods with an AI-based image analysis system that uses depth learning algorithms to automatically measure focal length and energy values from development patterns, achieving high measurement precision through computational analysis rather than physical measurement
Solution Approach 2:
The patent introduces an intermediary AI processing system that mediates between the development pattern images and the calibration parameters, using depth learning algorithms to extract precise focal length and energy values without direct physical measurement or manual intervention
Data Source
AI summary
A control equipment and a control method of a stepper are provided. The control equipment of the stepper includes an input device, a generating device and a processing device. The input device is configured to input a plurality of sample development patterns. The sample development patterns are obtained according to a plurality of sample focal length values. The generating device is configured to generate a plurality of generative categories corresponding to a plurality of generative focal length values by using a depth learning algorithm. The processing device is configured to analyze an estimated focal length value of the online development pattern according to the generative categories.