Ion Beam Shape Matching Using Regression-Guided Tuning Clusters
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Solution Overview
Problem
Existing methods for configuring ion beam generators to achieve desired ion beam shapes are slow and often identify sub-optimal configurations due to the vast number of possible parameter combinations and the unpredictability of changes to beam shape parameters.
Innovation Solution
A computer-implemented method that involves selecting exploratory points in a search space defined by tunable parameters, training a regression model to predict beam shape parameters for interpolated points, defining clusters based on predicted and measured parameters, evaluating clusters for stability and sensitivity, and outputting tuning settings for the selected cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional exhaustive search methods are used to configure ion beam generator parameters, then complete coverage of parameter space is achieved, but the configuration process becomes extremely time-consuming and slow
Solution Approach 1:
The system performs preliminary measurements at a limited set of exploratory points in the parameter space before full optimization. These preliminary actions provide initial data that guides subsequent optimization, avoiding the need to exhaustively search the entire parameter space and significantly reducing configuration time while maintaining accuracy.
Solution Approach 2:
The system uses measured beam shape parameters from exploratory points as feedback to train a regression model. This model then predicts optimal parameter settings, creating a feedback loop that accelerates the configuration process by learning from measurements rather than requiring exhaustive searching of all parameter combinations.
2Adaptability or versatility
If the number of tunable parameters is increased to achieve more complex beam shapes, then beam shape versatility is improved, but the complexity of the search space increases making optimization difficult
Solution Approach 1:
The system segments the high-dimensional parameter space into manageable regions by selecting a limited set of exploratory points. Each point represents a specific combination of tunable parameters, and the regression model learns the relationships within these segmented regions, making the complex search space tractable while maintaining the ability to achieve complex beam shapes.
Solution Approach 2:
The system changes the approach from directly searching through all parameter combinations to using a regression model that predicts optimal parameters based on measured data. This parameter transformation approach handles the complexity of multiple tunable parameters by learning their relationships rather than exhaustively searching them.
3Measurement precision
If measurements are taken at many different parameter combinations to ensure accurate beam shape control, then measurement accuracy is improved, but the number of required measurements increases significantly
Solution Approach 1:
The system creates a virtual model (regression model) that copies the relationship between parameters and beam shape characteristics. Once trained on a limited set of measurements, this model can predict beam shape parameters for any combination of tunable parameters, eliminating the need for exhaustive physical measurements while maintaining accuracy.
Solution Approach 2:
The system performs measurements at a partial set of exploratory points rather than at all possible parameter combinations. This partial action is sufficient to train the regression model, which then handles the remaining predictions, significantly reducing the total number of measurements required while maintaining configuration accuracy.
Data Source
AI summary
Techniques for adjusting the shape of an ion beam are described. Characteristics of a desired beam shape may be defined. The ion beam generator may include beam shaping elements associated with tunable parameters that can be set in combination with each other. A search space for the possible combinations is defined. A set of exploratory points in the search space are measured and used to interpolate a large number of interpolated points based on a regression model. Interpolated points that are associated with low confidence values may be measured. Based on the measured and interpolated points, clusters of tunable parameter combinations may be identified for evaluation. The clusters are evaluated for stability and sensitivity, and one of the clusters is selected based on the evaluation. The ion beam generator may be configured based on the selected cluster.