Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards

2025-09-09

AI Summary

Sensor placement optimization for structural health monitoring of semiconductor probe cards remains challenging. Method: This paper proposes a physics-informed Transformer-based deep learning framework. Leveraging frequency response function data generated via finite element simulation, the model jointly encodes dynamic response features and physical constraints by integrating convolutional layers with self-attention mechanisms. Physics-guided data augmentation and perception-aware statistical enhancement are introduced to improve generalizability, while attention-weight visualization identifies critical sensor locations. Contribution/Results: The method achieves 99.83% accuracy in health-state classification and 99.73% recall in crack detection. Robustness is validated through triple 10-fold stratified cross-validation. The approach delivers an interpretable, cost-effective sensor deployment paradigm for active maintenance systems, advancing predictive maintenance in semiconductor manufacturing.

Technology Category

Intelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Hardware-aware MLSearch and Optimization: Learning to Search

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents an innovative Transformer-based deep learning strategy for optimizing the placement of sensors aiming at structural health monitoring of semiconductor probe cards. Failures in probe cards, including substrate cracks and loosened screws, would critically affect semiconductor manufacturing yield and reliability. Some failure modes could be detected by equipping a probe card with adequate sensors. Frequency response functions from simulated failure scenarios are adopted within a finite element model of a probe card. A comprehensive dataset, enriched by physics-informed scenario expansion and physics-aware statistical data augmentation, is exploited to train a hybrid Convolutional Neural Network and Transformer model. The model achieves high accuracy (99.83%) in classifying the probe card health states (baseline, loose screw, crack) and an excellent crack detection recall (99.73%). Model robustness is confirmed through a rigorous framework of 3 repetitions of 10-fold stratified cross-validation. The attention mechanism also pinpoints critical sensor locations: an analysis of the attention weights offers actionable insights for designing efficient, cost-effective monitoring systems by optimizing sensor configurations. This research highlights the capability of attention-based deep learning to advance proactive maintenance, enhancing operational reliability and yield in semiconductor manufacturing.

Problem

• Optimizing sensor placement for structural health monitoring
• Detecting probe card failures like cracks and loose screws
• Enhancing semiconductor manufacturing yield and reliability

Innovation

• Transformer-based deep learning for sensor placement
• Hybrid CNN-Transformer model classifies health states
• Attention mechanism optimizes sensor configurations efficiently