Semiconductor Chamber Parameter Adjustment for Deposition Uniformity
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Solution Overview
Problem
The challenge in semiconductor manufacturing is achieving uniform electrochemical deposition on semiconductor wafers due to numerous input parameters, which requires multiple iterations and significant human intervention, often resulting in inefficiencies and wafer waste.
Innovation Solution
A system that incorporates a trained model and optimizer to automatically adjust operating parameters of the electrochemical deposition process, using measurements from a metrology station to refine the recipe for subsequent wafers, thereby reducing the need for human intervention and improving uniformity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of deposition parameters is used, then human intervention can optimize the process, but the number of iterations increases and wafer waste occurs
Solution Approach 1:
The system implements a closed-loop feedback mechanism where the neural network continuously receives measurement data from metrology stations about previous deposition results, processes this information, and automatically adjusts deposition parameters for subsequent wafers. This automated feedback loop eliminates manual intervention while maintaining or improving deposition uniformity across multiple wafers.
Solution Approach 2:
The neural network enables the electrochemical deposition system to self-optimize by automatically learning from measurement data and adjusting its own parameters without external human intervention. The system serves itself by autonomously improving deposition uniformity through machine learning algorithms that process measurement feedback and generate optimized deposition recipes.
2Manufacturing precision
If multiple iterations are performed to achieve optimal deposition, then manufacturing precision improves, but processing time increases
Solution Approach 1:
The neural network performs preliminary learning and parameter optimization during initial processing steps by analyzing measurement data from first and second wafers. This preliminary action allows the system to pre-calculate optimized parameters before processing subsequent wafers, thereby reducing the time needed for each additional iteration while maintaining thickness uniformity.
Solution Approach 2:
The system replaces manual mechanical adjustment processes with automated computational algorithms. The neural network uses machine learning to rapidly process measurement data and generate optimized deposition parameters, substituting time-consuming manual trial-and-adjustment cycles with fast computational optimization that maintains precision while reducing total processing time.
3Manufacturing precision
If human intervention is used to adjust parameters, then process optimization is achieved, but the extent of automation decreases
Solution Approach 1:
The neural network enables the system to self-optimize deposition parameters autonomously without human intervention. The automated system processes measurement data, learns optimal parameter settings through machine learning, and automatically adjusts deposition conditions, achieving both high automation and precise control over the electrochemical deposition process.
Solution Approach 2:
The system implements automated feedback loops where measurement data from metrology stations continuously informs the neural network, which then automatically adjusts deposition parameters. This closed-loop automated feedback mechanism maintains precise control over deposition uniformity while maximizing the extent of automation by eliminating manual parameter adjustment.
4Reliability
If traditional parameter adjustment methods are used, then process control is maintained, but productivity decreases due to manual intervention
Solution Approach 1:
The system replaces manual parameter adjustment mechanisms with automated neural network-based control. This substitution maintains reliable process control through consistent application of learned optimization patterns while dramatically improving productivity by eliminating time-consuming manual intervention between wafer processing cycles.
Solution Approach 2:
The neural network enables the deposition system to self-regulate and self-optimize parameters autonomously. This self-service capability maintains reliable process control through automated feedback and adjustment while increasing wafer processing throughput by eliminating manual intervention, thereby simultaneously improving both reliability and productivity.
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 optimal deposition, minimizing wafer waste and improving processing time by using a combination of a trained model and optimizer to adjust parameters in real-time.
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
a second semiconductor processing station configured perform measurements indicative of a thickness of the material after the material has been deposited on the first semiconductor wafer
Data Source
AI summary
A system may include a first semiconductor processing station configured to deposit a material on a first semiconductor wafer, a second semiconductor processing station configured perform measurements indicative of a thickness of the material after the material has been deposited on the first semiconductor wafer, and a controller. The controller may be configured to receive the measurements from the second station; 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 of the material is closer to a target thickness; and causing the first station to deposit the material on a second wafer using the operating parameter as adjusted by the output.