Micro-Object Movement Monitoring for Real-Time Orientation Tracking
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Solution Overview
Problem
Existing micro-assembler systems face challenges in efficiently processing large numbers of micro-objects moving across a surface, requiring fast and accurate tracking and orientation determination to facilitate real-time control, which is hindered by the complexity of image processing and the cost and time required for training deep-learning models.
Innovation Solution
A system utilizing a deep-learning computer vision algorithm to detect and track thousands of micro-objects in real-time, combined with a method to automatically generate training data with accurate ground truth, simplifying the image recognition process by focusing on location rather than orientation, and incorporating a machine learning based image processing model to infer movements based on electric force profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep-learning models are used to track micro-objects in real-time, then tracking accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The system pre-generates training data with accurate ground truth labels before deploying the deep-learning model. This preliminary preparation of training datasets with known micro-object locations and orientations enables the model to be trained more efficiently, reducing the time required for training while maintaining high tracking accuracy during real-time operation.
Solution Approach 2:
The system creates synthetic training data that replicates real-world scenarios with perfect ground truth information. By generating artificial images with known micro-object positions and orientations, the system avoids the time-consuming process of manually annotating real images, thus reducing training time while preserving measurement precision.
2Loss of information
If comprehensive image processing is performed to determine both location and orientation, then measurement completeness is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential features needed for tracking - specifically location and orientation of micro-objects - from the captured images. By focusing the image processing algorithm to detect only these critical parameters rather than analyzing all image characteristics, the system maintains measurement completeness for tracking purposes while significantly reducing processing time.
Solution Approach 2:
The image processing applies different analysis depths to different regions and features. Micro-objects are detected with high precision for location and orientation, while the surrounding background receives minimal processing. This localized quality approach ensures complete measurement of critical parameters without the computational overhead of processing the entire image comprehensively.
3Device complexity
If traditional image processing methods are used, then system complexity is reduced, but processing speed decreases
Solution Approach 1:
The system replaces traditional mechanical or rule-based image processing methods with a deep-learning-based computer vision algorithm. This substitution enables real-time processing of thousands of micro-objects by leveraging the parallel processing capabilities and pattern recognition strength of neural networks, dramatically increasing processing speed while managing system complexity through specialized hardware acceleration.
Solution Approach 2:
The system changes the processing parameters by using deep-learning models that can handle high-dimensional image data efficiently. By adjusting the model architecture, training parameters, and inference settings, the system achieves real-time processing speeds for thousands of micro-objects while keeping the overall system complexity manageable through optimized computational parameters.
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
Enables efficient and real-time control of micro-assembler stations by accurately tracking micro-object movements and orientations, reducing processing time to less than one-tenth of a second per image, and facilitating the placement of micro-objects into defined locations on a micro-assembler backplane.
Implementation Method 1
micro-objects to move using electrophoretic or dielectrophoretic force induced by the electrostatic fields generated across the dynamic electrostatic electrode actuator array
Implementation Method 2
micro-objects to move using electrophoretic or dielectrophoretic force induced by the electrostatic fields generated across the dynamic electrostatic electrode actuator array
Implementation Method 3
arrange micro-objects, such as microchips of various types, into a defined arrangement on a backplane according to a design of an apparatus being assembled
Data Source
AI summary
What is disclosed is methods and systems for monitoring micro-object movements. Images depicting micro-objects moving across a backplane is received. Based on processing by a machine learning based image processing model, a first location and orientation of each micro-object in a first image and a second location and orientation of each micro-object in a second image is determined. Based on estimates of forces applied during a time between capture of the first and second images, movement during the time of a micro-object in the first image is estimated. A likely image of the micro-object in the second image is inferred based on the likely image having a location and orientation in the second image that corresponds to a combination of the movement and the location and orientation of the micro-object in the first image. An indication that the micro-object in the first image corresponds to the likely image is output.