Ion Implanter Ribbon Beam Tuning for Uniform Dose and Angle
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Solution Overview
Problem
Conventional tuning techniques for ion implanters are slow, require numerous iterations, and often result in sub-optimal ribbon beam uniformity, leading to reduced throughput and non-uniformity in wafer implantation.
Innovation Solution
Employing machine learning (ML) techniques to rapidly select optimal configurations for tuning segments in ion implanters, using a Bayesian fitting technique to model the effects of tuning segment changes on beam dose and angles, and implementing a forward model to predict and adjust ion beam uniformity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional tuning techniques are used to adjust tuning segments, then beam uniformity can be improved, but the process requires numerous iterations and consumes excessive time
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing optimal tuning segment configurations for various beam profiles in a lookup table before actual operation. When tuning is needed, the system simply queries the pre-computed table rather than performing iterative adjustments, thus achieving fast beam uniformity optimization without time-consuming iterations.
Solution Approach 2:
The patent uses copying by creating a mathematical model that replicates the complex physics of ion beam extraction and propagation. This model allows the system to predict beam profiles and determine optimal tuning configurations through computation rather than physical trial-and-error, significantly reducing tuning time while maintaining precision.
2Manufacturing precision
If conventional iterative tuning methods are employed, then beam profile optimization can be achieved, but throughput is reduced due to repeated retuning
Solution Approach 1:
The system pre-computes optimal tuning configurations for multiple beam profiles and stores them in a lookup table. When a specific beam profile is required, the corresponding tuning parameters are immediately retrieved from the table, eliminating iterative retuning and enabling rapid switching between different implantation recipes, thus maintaining both precision and high throughput.
Solution Approach 2:
The patent replaces the mechanical iterative adjustment process with a computational system that uses mathematical modeling and lookup tables. Instead of physically adjusting tuning segments through multiple iterations, the system calculates optimal configurations computationally and implements them directly, substituting mechanical trial-and-error with electronic computation to improve throughput.
3Manufacturing precision
If conventional tuning approaches are used, then some beam uniformity can be achieved, but globally optimal tuning segment locations are not selected
Solution Approach 1:
The patent introduces an intermediary mathematical model that acts as a bridge between the physical tuning segments and the desired beam profile. This model incorporates the extraction field geometry and ion optics physics to accurately predict how each tuning segment configuration affects the beam, enabling the system to identify globally optimal segment locations rather than relying on local optimization through iteration.
Solution Approach 2:
The system systematically varies tuning segment parameters (positions, voltages) across their full ranges and evaluates their effects using the mathematical model. By exploring the complete parameter space rather than making incremental adjustments, the system identifies globally optimal configurations that achieve the best beam uniformity, avoiding local minima that conventional iterative methods might settle into.
Data Source
AI summary
Techniques for ribbon beam tuning segment tuning using machine learning are described. A method comprises receiving a set of control parameters representing configurations of multiple tuning segments of a tuning assembly for an ion implanter, predicting a set of process parameters representing one or more metrics associated with a beam property for an ion beam generated by the ion implanter based on the configurations of the multiple tuning segments using a control model, the control model comprising a forward model using a tuning matrix generated from a set of observations and a covariance matrix, and configuring a set of configurations for the multiple tuning segments based on the set of process parameters, the set of configurations for the multiple tuning segments to cause the ion beam to match a target metric for the ion beam. Other embodiments are described and claimed.