Wafer Immersion Timing via Liquid Surface Image Detection
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Solution Overview
Problem
In semiconductor processing, the removal of a previous wafer from an electrochemical deposition chamber causes surface disturbances like bubbles, foam, and contaminants, leading to nonuniform metal coatings on subsequent wafers due to the lack of a reliable method to determine when the liquid is ready for the next wafer, resulting in process irregularities and potential wafer failures.
Innovation Solution
A neural network is trained to analyze real-time images of the liquid surface captured by a camera, determining when the liquid is free of defects such as bubbles, ripples, and contaminants, allowing for optimized timing of wafer immersion and reducing the delay between processing cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the wafer is immediately immersed in the liquid after the previous wafer is removed, then the productivity is improved by reducing wait time, but the manufacturing precision deteriorates due to nonuniform metal coatings caused by surface disturbances
Solution Approach 1:
The system performs preliminary detection of liquid surface conditions using a camera and neural network analysis before deciding to immerse the wafer. This preliminary action allows the system to identify when the liquid surface has recovered from disturbances (bubbles, ripples, contaminants) and is ready for wafer immersion, thus maintaining coating uniformity while minimizing unnecessary wait time
Solution Approach 2:
The system implements a feedback mechanism where images of the liquid surface are continuously captured and analyzed by a trained neural network. The analysis results feed back into the process control system to determine the optimal timing for wafer immersion, creating a closed-loop control that adapts to real-time liquid surface conditions and prevents nonuniform coatings
2Manufacturing precision
If a fixed delay is applied between wafer removal and immersion to ensure liquid stability, then the manufacturing precision is improved by avoiding surface disturbances, but the productivity deteriorates due to extended wait time
Solution Approach 1:
The system replaces the static fixed delay approach with a dynamic, condition-based timing mechanism. The neural network continuously evaluates liquid surface conditions and provides real-time guidance on when immersion is appropriate. This dynamic approach allows the process to adapt to varying recovery rates of the liquid surface, optimizing both coating quality and processing speed for each individual cycle
3Manufacturing precision
If the neural network analysis is used to determine liquid readiness, then the manufacturing precision is improved by ensuring uniform coatings, but the device complexity increases due to additional imaging and processing components
Solution Approach 1:
The system replaces complex mechanical sensing methods with optical imaging and computational analysis. Instead of using multiple physical sensors to detect liquid surface conditions, a single camera captures images that are then analyzed by a neural network. This substitution of mechanical detection with optical-computational methods achieves precise liquid readiness determination while adding minimal hardware complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes the wait time between wafer processes, ensures uniform coatings, and reduces wafer failures by accurately determining when the liquid is ready for the next wafer, thereby improving the efficiency and consistency of the electroplating process.
Implementation Method 1
A neural network is trained to analyze real-time images of the liquid surface captured by a camera
Implementation Method 2
Electroplating uses electrodeposition to coat an object in a layer of metal. Generally, an anode and a cathode are placed in an electrolyte chemical bath and exposed to an electrical current.
Data Source
AI summary
A semiconductor processing chamber may process wafers by submerging the wafers in a liquid. To determine when the liquid is free of disturbances or contaminants and thus ready to receive the next wafer, a camera may be positioned to capture images of the liquid after a wafer has been removed from the liquid. A controller may provide the images of the liquid to a neural network to determine when the liquid is ready based on an output of the neural network. The neural network may be trained to identify disturbances, such as ripples, bubbles, or contaminants in the liquid. The controller may then begin controlling the next semiconductor process and submerge the next wafer.