Micro-Tooling Workflow Control Using Sequential Machine Learning
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Solution Overview
Problem
The operation of micro-tooling devices is complex and error-prone due to a large number of operational parameters and parameter drift, requiring skilled personnel and limiting their availability and throughput, with potential for sample or device damage from human error.
Innovation Solution
A method using two sequentially coupled machine-learning algorithms to predict future settings of operational parameters based on image analysis, allowing for automated control of micro-tooling devices by determining progress along a workflow and adjusting settings accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual control by skilled personnel is used, then operational accuracy is maintained, but device availability and throughput are limited
Solution Approach 1:
The micro-tooling device performs self-adjustment of operational parameters through integrated sensors and control algorithms that automatically detect sample properties and modify settings without human intervention, enabling the system to serve itself and eliminate dependency on skilled operators
Solution Approach 2:
The system automatically modifies operational parameters such as acceleration voltage, beam current, and stage position based on real-time feedback from sensors and pre-defined workflows, allowing dynamic adaptation to different sample types and experimental conditions without manual reconfiguration
2Adaptability or versatility
If skilled personnel operate the device, then errors are minimized, but the device is restricted to specific tasks and locations
Solution Approach 1:
The automated control system with pre-defined workflows for multiple imaging modes (SEM, TEM, STEM) and manipulation tasks enables the single device to perform diverse functions across different applications and locations, eliminating the need for specialized operators for each task type
Solution Approach 2:
Sensors continuously monitor operational parameters and sample conditions, feeding this information back to the control system which automatically adjusts settings to maintain optimal performance and prevent errors, ensuring consistent reliability regardless of operator skill level
3Reliability
If manual adjustment of operational parameters is performed, then parameter drift can be corrected, but the process becomes complex and error-prone
Solution Approach 1:
Manual mechanical adjustment of operational parameters is replaced by an automated electronic control system with pre-programmed workflows and algorithms that systematically manage parameter changes, eliminating the complexity and human error associated with manual control while maintaining operational stability
Data Source
AI summary
A micro-tooling device, such as, for example, a scanning electron microscope or a focused-ion beam microscope, provides images. A first machine-learning algorithm and a second machine-learning algorithm are sequentially coupled. The first machine-learning algorithm determines a progress along a predefined workflow based on feature recognition in images associated with the workflow. The second machine-learning algorithm predicts settings of operational parameters of the micro-tooling device in accordance with the progress along the predefined workflow.