AFM Cantilever Image Recognition for Precise Laser Targeting
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Solution Overview
Problem
Existing atomic force microscopes face challenges in accurately identifying a target position for scanning due to variations in cantilever size and shape, which affects the precision of sample imaging.
Innovation Solution
An apparatus and method utilizing an artificial neural network model to identify the cantilever's target position by analyzing images of the cantilever's upper surface, employing bounding box and segmentation data to calculate the precise position for laser light irradiation, regardless of cantilever dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify target position on cantilever, then the process is simple, but the identification accuracy deteriorates due to variations in cantilever size and shape
Solution Approach 1:
The patent replaces traditional mechanical/optical alignment methods with an artificial neural network-based image recognition system. The control unit captures images of the cantilever and uses a pre-trained neural network model to automatically identify the target position, substituting manual or rule-based mechanical positioning with intelligent image processing.
Solution Approach 2:
The neural network model is trained on diverse cantilever images with varying sizes, shapes, and orientations. By learning from multiple parameter variations during training, the model adapts to different cantilever configurations and accurately identifies target positions regardless of specific dimensional variations.
2Productivity
If manual alignment methods are used, then the system complexity is low, but the operation speed and efficiency deteriorate
Solution Approach 1:
The system performs self-alignment by automatically capturing images of the cantilever and using the neural network to identify the target position without requiring manual intervention. The control unit autonomously completes the positioning task, improving operation speed and eliminating manual alignment steps.
Solution Approach 2:
Manual alignment operations are replaced with automated image capture and neural network processing. The system uses a camera to capture cantilever images and the neural network to automatically determine target position, substituting slow manual procedures with fast automated computational methods.
3Adaptability or versatility
If fixed alignment procedures are used, then the process is straightforward, but the adaptability to different cantilever specifications deteriorates
Solution Approach 1:
The neural network model serves as a universal identification system that handles various cantilever types, sizes, and shapes through a single unified approach. The model is trained on diverse data and can generalize to different cantilever specifications, providing multi-functional adaptability without requiring separate alignment procedures for each cantilever type.
Solution Approach 2:
The system adapts to different cantilever parameters by learning from training data that includes various sizes, shapes, and configurations. The neural network adjusts its internal parameters during training to recognize patterns across different cantilever specifications, enabling flexible adaptation without changing the underlying system architecture.
Data Source
AI summary
Provided are an apparatus and a method for identifying a target position in an atomic microscope. An apparatus is configured to acquire result data identifying the cantilever from an image using an identification model learned to identify the cantilever based on the image photographed by a photographing unit, and calculate a target position from the cantilever using the acquired result data, in which the result data include at least one of bounding box data representing a bounding box including a boundary of the cantilever and segmentation data obtained by segmenting the cantilever and an object other than the cantilever.