TSV (Through-Silicon Via) is a vertical interconnection structure achieved by creating holes in the silicon substrate of a chip. This paper explores the subtle differences in the reflection characteristics of light across various TSV structures. Electromagnetic simulation software, namely, Lumerical FDTD, based on the Finite-Difference Time-Domain (FDTD) method, was employed to establish electromagnetic models for TSV structures. 3D-FDTD simulations calculate near-field electromagnetic field data and derive near-field reflection spectra. The dataset comprises different TSV structures with varying critical dimensions (CD) and their corresponding far-field reflection spectra obtained through simulation. Our approach introduces two deep learning networks: the forward network and the inverse network. Given the time-consuming nature of FDTD electromagnetic simulation tools when dealing with complex structures, the forward network is trained to rapidly predict reflection spectrum signal data corresponding to CD parameters, effectively replacing simulation software. Using a threshold of 1% to determine the accuracy of the spectral predictions, forward prediction achieves 100% accuracy. Similarly, the inverse network is trained to predict the CD parameters of TSV structures from the reflection spectrum signal, allowing for fast and accurate inference of the dimensions of TSV structures and the extraction of geometric parameters. In reverse prediction, the MAPE (Mean Absolute Percentage Error) for Rtop, height, and Rbot remains consistently below 5%.
KEYWORDS: Education and training, Data modeling, Defect detection, Inspection, Titanium, Holmium, Light sources and illumination, Performance modeling, Cameras, Windows
In the field of automatic defect detection, a major challenge in training accurate classifiers using supervised learning is the insufficient and limited diversity of datasets. Obtaining an adequate amount of image data depicting defective surfaces in an industrial setting can be costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in an underrepresentation of certain defect classes. Our research aims to address surface defect detection in titanium metal spacer rings by introducing an approach leveraging a digital twin framework. The behavior of the digital twin is optimized using a reinforcement learning (RL) algorithm. The optimized digital twin is then used to generate synthetic data, which is employed to train a spacer defect detection classifier. The classifier’s performance is evaluated using real-world data. The results show that the model trained with synthetic data outperforms the one trained on a limited amount of real data. Our work highlights the potential of digital twin-based synthetic data generation and RL optimization in enhancing spacer surface defect detection and addressing the data scarcity challenge in the field. When the inspection network is trained solely using generated synthetic data, it achieves an inspection precision of 96.10%, with a recall of 85.77%. Incorporating real data alongside synthetic data for training further enhances performance, yielding an inspection precision of 93.07% and a recall of 94.20%. This surpasses the defect detection capabilities observed when training the inspection network exclusively with real data.
In the field of automatic defect detection, one of the major challenges for training accurate classifiers using supervised learning is the insufficient and limited diversity of datasets. Obtaining an adequate amount of image data depicting defective surfaces in an industrial setting is costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in underrepresentation of certain defect classes. This research aims to tackle the issue of surface defect detection in titanium metal spacer rings by introducing a novel approach that leverages a digital twin framework. The behavior of the digital representation is optimized using a reinforcement learning algorithm. Subsequently, the optimized digital twin is utilized to generate synthetic data, which is then employed to train a spacer defect detection classifier. The performance of this classifier is evaluated using real-world data. The results illustrate that the model trained with synthetic data outperforms the one trained on a limited amount of real data. This work emphasizes the potential of digital twin-based synthetic data generation and reinforcement learning optimization in enhancing spacer surface defect detection and addressing the data scarcity challenge in the field. When the generated synthetic data and real data combined is used to train inspection network, the inspection background accuracy reaches 93.07% and defect detection accuracy reaches 94.2% surpassing the defect detection performance of inspection network trained only using real data.
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