Learning-Based Image Alignment Across Modalities
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Solution Overview
Problem
Current methods for aligning images from different modalities in semiconductor manufacturing, such as heuristic and physics-based approaches, face challenges like complexity, hardware dependency, and limited knowledge sharing due to their heuristic nature and reliance on specific imaging simulation models, leading to errors and increased development burdens.
Innovation Solution
A learning-based system that uses a computer model to transform and align images from different modalities into a common space, leveraging deep learning techniques to overcome the limitations of existing methods by providing a data-driven, hardware-independent solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heuristic or physics-based approaches are used for aligning images from different modalities, then alignment can be performed using traditional methods, but the system becomes complex and hardware-dependent with limited knowledge sharing
Solution Approach 1:
The patent replaces traditional heuristic and physics-based mechanical alignment methods with a learning-based computational approach. The system uses trained models to automatically align images from different modalities, eliminating the need for complex hardware-dependent algorithms and enabling knowledge sharing across different imaging systems through transfer learning.
Solution Approach 2:
The learning-based alignment system is designed to be modality-agnostic and can align images from any combination of imaging modalities (optical, electron, acoustic, etc.). The same framework and trained models can be applied across different hardware platforms and imaging techniques, providing universal functionality without requiring modality-specific algorithms.
2Ease of manufacture
If heuristic or physics-based alignment methods are used, then traditional alignment can be achieved, but errors increase and development burden grows
Solution Approach 1:
The system performs preliminary training of alignment models using paired images from different modalities before actual alignment tasks. This pre-training phase captures the transformation characteristics between modalities, enabling accurate and reliable alignment during operational use without requiring complex real-time computations or manual adjustments.
Solution Approach 2:
The learning-based system incorporates feedback mechanisms where alignment results are continuously evaluated and used to refine the models. The system can learn from misalignments and improve performance over time, reducing errors and increasing reliability while maintaining ease of implementation through automated optimization.
3Adaptability or versatility
If traditional alignment methods are used, then existing algorithms can be applied, but adaptability to new challenges is limited requiring extensive algorithm tweaking
Solution Approach 1:
The alignment system is designed to be dynamic and adaptable to new imaging modalities and challenges. Rather than requiring static, pre-programmed algorithms for each modality combination, the system can dynamically adjust by applying transfer learning and fine-tuning existing models to new scenarios, maintaining low algorithmic complexity while achieving high versatility.
Solution Approach 2:
The patent introduces a universal feature representation space as an intermediary between different imaging modalities. Instead of directly comparing raw images from different modalities, the system transforms them into a common feature space where alignment can be performed using simple, general-purpose algorithms, enabling adaptability without increasing complexity.
Data Source
AI summary
Methods and systems for aligning images for a specimen acquired with different modalities are provided. One method includes acquiring information for a specimen that includes at least first and second images for the specimen. The first image is acquired with a first modality different than a second modality used to acquire the second image. The method also includes inputting the information into a learning based model. The learning based model is included in one or more components executed by one or more computer systems. The learning based model is configured for transforming one or more of the at least first and second images to thereby render the at least the first and second images into a common space. In addition, the method includes aligning the at least the first and second images using results of the transforming. The method may also include generating an alignment metric using a classifier.