AI Component Placement for PCBs Using Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing complexity and demand for CPU SKUs in PCB component placement pose challenges for engineers, as existing auto-placement tools are not suitable for complex circuit designs, requiring skilled engineers and taking extensive time, while also raising data privacy concerns.
Innovation Solution
An AI-based component placement technology using parameterized placement procedures and federated learning to capture design constraints and user preferences, optimizing component placement by minimizing overlap, area, and net-length, while protecting data privacy through local data storage and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If skilled engineers manually place components, then placement quality is high, but placement time is extensive and labor cost is high
Solution Approach 1:
The patent replaces manual mechanical placement operations with an AI-based automated system. The machine learning model analyzes circuit designs and automatically determines optimal component positions, substituting the manual engineering process with an intelligent automated system that achieves both high quality and reduced time.
Solution Approach 2:
The system changes the approach from manual parameter adjustment to AI-driven parameter optimization. The machine learning model processes multiple design parameters simultaneously (component positioning, routing, constraints) and outputs optimized placement solutions, transforming the manual parameter tuning process into an automated optimization process.
2Loss of time
If existing auto-placement tools are used, then placement time is reduced, but placement quality deteriorates for complex circuit designs
Solution Approach 1:
The patent incorporates feedback mechanisms where the AI model continuously evaluates placement solutions against multiple criteria (design rules, constraints, optimization objectives) and iteratively refines the placement. This feedback loop enables the system to achieve high placement quality while maintaining fast automated processing, overcoming the limitations of traditional auto-placement tools.
Solution Approach 2:
The system advances from fixed or simple auto-placement algorithms to a dynamic AI-based optimization system that adapts to complex circuit designs. The machine learning model processes and optimizes multiple parameters simultaneously, enabling high-quality placement for complex designs while maintaining automated processing speeds.
3Measurement precision
If data is shared for model training, then AI model accuracy improves, but data privacy concerns arise
Solution Approach 1:
The patent introduces federated learning as an intermediary approach between data sharing and model training. Instead of centralizing data for training, the system enables distributed training across multiple devices while keeping data local, thus improving model accuracy without compromising data privacy. This intermediary technique resolves the contradiction by enabling collaborative learning without direct data exposure.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that receives parameter results and scores of one or more local sessions with respect to subcircuit components in a bounded area, aggregates the parameter results and scores, and generates a global placement model based on an output of the aggregated parameter results and scores.