Deposition Parameter Control for Wafer Thickness Uniformity
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Solution Overview
Problem
The challenge in semiconductor manufacturing is achieving uniform electrochemical deposition of metal films on wafers, as existing techniques require multiple iterations and significant human intervention to optimize input parameters, often resulting in suboptimal solutions and inefficiencies.
Innovation Solution
A system that integrates sensors to measure characteristics of the chemical bath and wafer, using a trained model to adjust operating parameters, such as anode current and process time, to achieve target thickness uniformity, thereby automating the optimization process and reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual optimization techniques are used to achieve uniform electrochemical deposition, then deposition uniformity can be improved, but the number of iterations and human intervention required increases significantly
Solution Approach 1:
The system implements a closed-loop feedback mechanism where sensors continuously measure deposition thickness and uniformity, feed this data to a trained model, which then automatically adjusts operating parameters for the next deposition cycle. This eliminates manual intervention and reduces iterations by providing real-time feedback and automatic correction.
Solution Approach 2:
The system enables self-service through automation where the trained model independently optimizes deposition parameters without human intervention. The model receives sensor data, processes it, and automatically adjusts operating parameters, allowing the system to self-correct and self-optimize across multiple deposition cycles.
2Manufacturing precision
If multiple iterations are performed to optimize deposition parameters, then deposition uniformity improves, but wafer waste increases
Solution Approach 1:
The system performs preliminary action by using a trained model to predict optimal deposition parameters before actual deposition occurs. The model is pre-trained on historical data and sensor measurements, allowing it to anticipate the best parameters for achieving uniform deposition, thereby reducing trial-and-error iterations and associated wafer waste.
Solution Approach 2:
Real-time feedback from sensors during deposition allows the system to detect deviations from target uniformity and automatically adjust parameters for subsequent wafers, preventing further waste and ensuring consistent quality without requiring multiple full iterations on wasted wafers.
3Manufacturing precision
If traditional optimization methods are used, then deposition uniformity can be achieved, but processing efficiency decreases
Solution Approach 1:
The system replaces manual mechanical optimization processes with an automated intelligent system. A trained model (AI/ML system) substitutes for human operators who would manually adjust parameters based on experience and trial-and-error, enabling faster, more consistent optimization and significantly improving processing efficiency.
Solution Approach 2:
The system dynamically changes operating parameters based on real-time sensor feedback and model predictions. Instead of using fixed or manually adjusted parameters, the system continuously optimizes parameters such as current density, deposition time, and chemical composition, enabling faster convergence to optimal settings and improving overall processing efficiency.
4Ease of operation
If manual intervention is used to adjust parameters, then some level of control is maintained, but automation extent decreases
Solution Approach 1:
The system implements self-service by enabling automatic parameter adjustment through a trained model that independently processes sensor data and optimizes deposition parameters without human intervention. This maximizes automation extent while maintaining ease of operation through the user-friendly interface for monitoring and high-level control.
Solution Approach 2:
The closed-loop feedback system automatically monitors deposition progress and adjusts parameters in real-time, eliminating the need for continuous manual intervention. The system provides operators with feedback displays and alerts, maintaining ease of operation through monitoring capabilities while achieving high automation in the actual parameter adjustment process.
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 significantly reduces the number of iterations required to achieve desired uniformity, improving processing efficiency and reducing wafer waste by allowing for real-time adjustments based on measured data, thus enhancing the precision and speed of semiconductor processing.
Implementation Method 1
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. Electricity causes negatively charged anions to move to the anode and positively charged cations to be transferred to the cathode.
Implementation Method 2
The chemical tank may include one or more sensors that measure characteristics of the liquid. The measurements from the one or more sensors of the chemical tank may include a conductivity or resistivity measurement of the material.
Data Source
AI summary
A system may include a first semiconductor processing station configured to deposit a material on a first semiconductor wafer and a chemical tank that provides liquid to the processing station during a deposition process. The chemical tank may provide measurements of characteristics of the liquid to a controller. The controller may be configured to receive the measurements from the chemical tank; provide an input based on the measurements to a trained model that is configured to generate an output that adjusts an operating parameter of the first station such that the thickness uniformity of the material is closer to a target thickness uniformity; and cause the first station to deposit the material on a second wafer using the operating parameter as adjusted by the output.