Beam Scanning Control System for Dynamic 2D Pattern Optimization
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Solution Overview
Problem
Traditional beam scanning methods are inefficient as they waste measurements on regions with low geometry, require multiple scans for large FOVs, and trade off precision and speed, leading to suboptimal scanning performance.
Innovation Solution
A beam scanning control system that uses a motion compiler to generate a 2D scanning pattern based on high-level descriptive language, allowing dynamic control of scanning parameters such as beam trajectory, speed, and measurement acquisition rate, thereby optimizing scanning efficiency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional beam scanning methods are used to scan large field-of-view areas, then the scanning speed increases, but the measurement precision decreases due to wasted measurements on regions with low geometry
Solution Approach 1:
The patent applies local quality by implementing region-specific scanning parameters where different scanning regions (e.g., regions of interest versus low-geometry regions) have customized scanning patterns, measurement densities, and acquisition rates. This allows high precision in critical areas while maintaining overall scanning speed by reducing measurements in less important regions.
Solution Approach 2:
The system dynamically adjusts scanning parameters during operation based on real-time conditions. The controller modifies beam trajectory, scanning frequency, and measurement acquisition rate on-the-fly to optimize the balance between scanning speed and precision, rather than using fixed traditional scanning patterns.
2Area of stationary object
If traditional beam scanning methods are used to cover large field-of-view areas, then the scanning area increases, but the scanning time increases due to requiring multiple scans
Solution Approach 1:
The patent segments the field-of-view into multiple scanning regions with different priorities and characteristics. By dividing the large area into manageable zones (e.g., high-priority regions of interest and low-priority background regions), the system can apply optimized scanning patterns to each segment, reducing the total number of scans needed to cover the entire area effectively.
Solution Approach 2:
The system applies partial action by focusing measurement resources on critical regions rather than uniformly scanning the entire field-of-view. High-priority regions receive intensive scanning while low-priority regions receive minimal or no scanning, eliminating wasted time on areas that do not require detailed measurement.
3Device complexity
If traditional beam scanning methods are used with fixed measurement acquisition rates, then the system complexity decreases, but the scanning efficiency decreases due to unnecessary measurements
Solution Approach 1:
The control system dynamically adjusts measurement acquisition rates based on region priority and geometric complexity. Rather than using a fixed acquisition rate, the controller modifies the rate in real-time according to the specific scanning region being scanned, optimizing efficiency without requiring overly complex manual configuration.
Solution Approach 2:
The system performs self-optimization by automatically identifying regions of interest and adjusting scanning parameters based on detected geometric features. The controller autonomously determines which regions require detailed measurement and which can be scanned more quickly, eliminating the need for complex pre-programming while maximizing scanning efficiency.
Data Source
AI summary
A beam scanning control system includes a motion compiler, a localization assembler, and a controller. The motion compiler receives a high-level descriptive language defining a plurality of scanning regions of a scanning area and a plurality of scanning parameter subsets corresponding to the plurality of scanning regions, calculates a two-dimensional (2D) scanning pattern based on the high-level descriptive language, and generates assembly instructions based on the 2D scanning pattern. Each scanning parameter subset corresponds to a respective scanning region of the plurality of scanning regions. The localization assembler compiles the assembly instructions into machine instructions based on one or more characteristics of a 2D scanning system. The controller executes the machine instructions, and, based on executing the machine instructions, generates control signals for controlling the 2D scanning system to perform a 2D scan of the scanning area according to the 2D scanning pattern.