PCB Trace Boundary Analysis for Short-Circuit Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting undesired connections forming short circuits on printed circuit boards (PCBs) require a layout file of a defect-free reference circuit or a large amount of image data for training, which can be cumbersome and resource-intensive.
Innovation Solution
A method involving binarization of an original image to generate a binary image with an external boundary, identifying trace areas, and determining if they contact the boundary at four places to detect short circuits, without needing a layout file or extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a layout file of a defect-free reference circuit is used for detection, then detection accuracy is improved, but the complexity of the detection system increases and requires additional reference data
Solution Approach 1:
The detection method uses the PCB image itself to determine trace boundaries and detect short circuits, without requiring external layout files or reference data. The algorithm automatically identifies trace areas and their boundaries directly from the captured image, making the system self-sufficient and eliminating the need for additional reference materials.
Solution Approach 2:
The method extracts only the essential features needed for detection (trace areas and their boundaries) directly from the PCB image, discarding unnecessary complexity of layout files. By focusing solely on the visual characteristics of traces and their contact points with image boundaries, the system achieves detection without extraneous reference data.
2Reliability
If machine learning-based detection is used, then detection capability is improved, but the requirement for large amounts of training data increases
Solution Approach 1:
The patent replaces expensive and resource-intensive machine learning models with a simple, lightweight algorithm that requires no training data. The detection method uses basic image processing operations (binarization, boundary detection) that can be executed with minimal computational resources and no prior training, effectively using a 'disposable' simple approach instead of a complex trained model.
Solution Approach 2:
The method changes the detection parameters from complex machine learning model weights to simple geometric criteria (trace area contact points with image boundary). By transforming the detection problem into a geometric analysis task rather than a pattern recognition task, the system achieves reliable detection without requiring large training datasets.
3Measurement precision
If conventional detection methods are used, then detection accuracy is improved, but the detection time and processing efficiency worsen
Solution Approach 1:
The detection process is segmented into simple, discrete steps: binarization of the image, identification of trace areas, and counting of boundary contact points. Each step is computationally lightweight and can be executed quickly, maintaining detection accuracy while significantly reducing processing time compared to conventional methods.
Data Source
AI summary
A method for detecting an undesired connection forming a short circuit on a printed circuit board (PCB) based on an original image of the PCB includes steps of: performing binarization on the original image to generate a binary image that has an external boundary; determining, on the binary image, a trace area that corresponds to a trace of the PCB; determining whether the trace area contacts the external boundary of the binary image at four places; and determining that the trace that corresponds to the trace area has an undesired connection forming a short circuit when it is determined that the trace area contacts the external boundary of the binary image at four places.