PCB Netlist Extraction Using Multimodal Imagery
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Solution Overview
Problem
Existing non-destructive analysis (NDA) techniques fail to accurately and efficiently capture the connectivity of components on printed circuit boards (PCBs) in Commercial Off-The-Shelf (COTS) items, making it difficult to confirm compliance with design and performance specifications, especially when manufacturers do not provide design information.
Innovation Solution
A multimodal inspection system (MIS) using multiple electromagnetic wavelengths, such as infrared and ultraviolet radiation, combined with machine learning algorithms, to extract a netlist from PCBs by identifying components, their mounting points, and copper portions, overcoming occlusions and silkscreen interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-destructive analysis techniques are used to inspect PCBs, then the PCB components remain intact and can be reused, but the accuracy and efficiency of capturing component connectivity is insufficient
Solution Approach 1:
The inspection process is divided into multiple sequential stages: initial image capture, preprocessing to remove occlusions, netlist extraction, and verification. This segmentation allows each stage to be optimized independently, maintaining component integrity while improving connectivity capture accuracy through specialized processing at each step.
Solution Approach 2:
Multiple imaging modalities (X-ray, optical, infrared) are used as intermediaries to capture different aspects of the PCB. Each modality provides complementary information that, when combined, enables accurate connectivity mapping without physical contact with the components, thus maintaining integrity while improving measurement precision.
2Measurement precision
If multiple imaging modalities are used to capture PCB features, then the accuracy of netlist extraction is improved, but the system complexity and processing time increase
Solution Approach 1:
Multiple imaging modalities are merged into a unified inspection system with integrated data processing. The system combines X-ray, optical, and infrared images into a single comprehensive dataset, using automated algorithms to fuse the information and extract the netlist, thereby improving accuracy while managing complexity through integration rather than separate independent systems.
Solution Approach 2:
The system replaces manual inspection and analysis with automated machine learning algorithms and computer vision techniques. This substitution handles the complexity of processing multiple imaging modalities automatically, extracting netlist information without requiring complex manual intervention, thus improving precision while keeping the operational complexity manageable.
3Productivity
If traditional single-modality imaging is used, then the system is simpler and faster, but it cannot accurately extract netlists in the presence of occlusions and decorative text
Solution Approach 1:
The system adds temporal and spectral dimensions to the imaging process by capturing images at multiple time points and using different spectral modalities (X-ray, optical, infrared). This multi-dimensional approach allows the system to see through occlusions and decorative text that appear in single-modality images, improving netlist extraction accuracy while maintaining processing efficiency through automated parallel processing.
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
The MIS provides high accuracy and efficiency in generating a netlist, enabling verification of PCB connectivity and assembly integrity without destructive analysis, ensuring compliance with design specifications.
Implementation Method 1
A multimodal inspection system (MIS) using multiple electromagnetic wavelengths, such as infrared and ultraviolet radiation
Implementation Method 2
A multimodal inspection system (MIS) using multiple electromagnetic wavelengths, such as infrared and ultraviolet radiation
Data Source
AI summary
A system and method for generating a netlist for a printed circuit board. In some embodiments, the method includes capturing image data of a circuit board from a plurality of sensors to generate a set of captured data for each of the plurality of sensors; extracting a plurality of features from the image data using machine learning; and generating a design associated with the circuit board from the plurality of features.