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1. Ontology-guided Data Sharing and Federated Quality Control with Differential Privacy in Additive Manufacturing

   https://doi.org/10.1115/1.4067086  

   Yhdego, Tsegai; Wang, Hui; Chi, Hongmei; Yu, Zhibin (November 2024, Journal of Computing and Information Science in Engineering)

   Wang, Yan; Yang, Hui (Ed.)

   Abstract The scarcity of measured data for defect identification often challenges the development and certification of additive manufacturing processes. Knowledge transfer and sharing have become emerging solutions to small-data challenges in quality control to improve machine learning with limited data, but this strategy raises concerns regarding privacy protection. Existing zero-shot learning and federated learning methods are insufficient to represent, select, and mask data to share and control privacy loss quantification. This study integrates differential privacy in cybersecurity with federated learning to investigate sharing strategies of manufacturing defect ontology. The method first proposes using multilevel attributes masked by noise in defect ontology as the sharing data structure to characterize manufacturing defects. Information leaks due to the sharing of ontology branches and data are estimated by epsilon differential privacy (DP). Under federated learning, the proposed method optimizes sharing defect ontology and image data strategies to improve zero-shot defect classification given privacy budget limits. The proposed framework includes (1) developing a sharing strategy based on multilevel attributes in defect ontology with controllable privacy leaks, (2) optimizing joint decisions in differential privacy, zero-shot defect classification, and federated learning, and (3) developing a two-stage algorithm to solve the joint optimization, combining stochastic gradient descent search for classification models and an evolutionary algorithm for exploring data-sharing strategies. A case study on zero-shot learning of additive manufacturing defects demonstrated the effectiveness of the proposed method in data-sharing strategies, such as ontology sharing, defect classification, and cloud information use. 

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2. Deep Image Segmentation for Defect Detection in Photo-lithography Fabrication

   https://doi.org/10.1109/ISQED57927.2023.10129372  

   Paul, Omari; Abrar, Sakib; Mu, Richard; Islam, Riadul; Samad, Manar D. (July 2023, 2023 24th International Symposium on Quality Electronic Design (ISQED))

   Abstract: Surface acoustic wave (SAW) sensors with increasingly unique and refined designed patterns are often developed using the lithographic fabrication processes. Emerging applications of SAW sensors often require novel materials, which may present uncharted fabrication outcomes. The fidelity of the SAW sensor performance is often correlated with the ability to restrict the presence of defects in post-fabrication. Therefore, it is critical to have effective means to detect the presence of defects within the SAW sensor. However, labor-intensive manual labeling is often required due to the need for precision identification and classification of surface features for increased confidence in model accuracy. One approach to automating defect detection is to leverage effective machine learning techniques to analyze and quantify defects within the SAW sensor. In this paper, we propose a machine learning approach using a deep convolutional autoencoder to segment surface features semantically. The proposed deep image autoencoder takes a grayscale input image and generates a color image segmenting the defect region in red, metallic interdigital transducing (IDT) fingers in green, and the substrate region in blue. Experimental results demonstrate promising segmentation scores in locating the defects and regions of interest for a novel SAW sensor variant. The proposed method can automate the process of localizing and measuring post-fabrication defects at the pixel level that may be missed by error-prone visual inspection. 

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3. Synthetic data augmentation to enhance manual and automated defect detection in microelectronics

   https://doi.org/10.1016/j.microrel.2023.115220  

   Phoulady, Adrian; Suleiman, Yara; Choi, Hongbin; Moore, Toni; May, Nicholas; Shahbazmohamadi, Sina; Tavousi, Pouya (November 2023, Microelectronics Reliability)

   Failure analysis and defect detection are crucial processes in industries, governments, and societies to mitigate the risks associated with defective microelectronics. The accurate identification of faulty parts is vital for preventing potential damages. However, traditional manual and automated defect detection approaches face challenges due to the scarcity of ground truth data from defective parts. This limitation hampers the effectiveness of subject matter experts and machine learning models in recognizing and classifying new instances of defects. To address this issue, we propose a synthetic data augmentation workflow that generates virtual defective parts, effectively overcoming the data scarcity problem and enabling the creation of large datasets at a low cost. Our approach enhances defect detection capabilities, empowering industries and governments to improve the quality and reliability of electronic devices. 

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4. Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks

   https://doi.org/10.1109/3DV.2019.00051  

   Zhang, Ling; Zhu, Zhigang (September 2019, Proceedings - 2019 International Conference on 3D Vision (3DV))

   To alleviate the cost of collecting and annotating large-scale "3D object" point cloud data, we propose an unsupervised learning approach to learn features from an unlabeled point cloud dataset by using part contrasting and object clustering with deep graph convolutional neural networks (GCNNs). In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a "part" dataset. Then a contrast learning GCNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GCNN for clustering learning (ClusterNet) is trained from the orignal 3D data to predict the cluster IDs of all the training samples. The contrasting learning forces the ContrastNet to learn semantic features of objects, while the ClusterNet improves the quality of learned features by being trained to discover objects that belong to the same semantic categories by using cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtains comparable performance to the state-of-the-art with heavier shape auto-encoding unsupervised feature extraction methods. We have also tested the networks on object recognition using partial 3D data, by simulating occlusions and perspective views, and obtained practically useful results. The code of this work is publicly available at: https://github.com/lingzhang1/ContrastNet. 

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5. Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network

   Zhang, Ling; Zhu, Zhigang (June 2019, CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics)

   To alleviate the cost of collecting and annotating large- scale point cloud datasets for 3D scene understanding tasks, we propose an unsupervised learning approach to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with deep graph neural networks (GNNs). In the contrast learn- ing step, all the samples in the 3D object dataset are cut into two parts and put into a ”part” dataset. Then a contrast learning GNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GNN for cluster- ing learning (ClusterNet) is trained to predict the cluster IDs of all the training samples. The contrasting learning forces the ContrastNet to learn high-level semantic features of objects but probably ignores low-level features, while the ClusterNet improves the quality of learned features by be- ing trained to discover objects that belong to the same se- mantic categories by using cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtained comparable performance to the state-of-the-art unsupervised learning methods that used much more complicated network structures. The code and an extended version of this work is publicly available via: https://github.com/lingzhang1/ContrastNet 

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