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1. 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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2. 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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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. Defect localization in plate structures using the geometric phase of Lamb waves

   https://doi.org/10.1016/j.ultras.2024.107492  

   Zhang, Guangdong; Kundu, Tribikram; Deymier, Pierre A; Runge, Keith (January 2025, Ultrasonics)

   Commonly used methods for defect localization in structures are based on velocity differences (VD) or amplitude ratio (AR) (or attenuation due to scattering) measured along different sensing paths between a reference system and a defective system. A high value on a sensing path indicates a higher probability of the presence of defect on that path. We introduce an alternative approach based on the newly developed topological acoustic (TA) sensing technique for localizing defects in plate structures using Lamb waves. TA sensing exploits changes in geometric phase of acoustic waves to detect perturbations in the supporting medium. This approach uses a geometric phase change – index (GPC-I), a measure of the geometry of the acoustic field averaged over a spectral domain, as detection metric in lieu of VD or AR. Calculations based on the finite element method (FEM) in Abaqus/CAE software verifies the effectiveness of the proposed GPC-I-based defect localization method. Randomly located defects on the surface of a plate are localized with higher sensitivity and accuracy, by the GPC-I method in comparison to VD or AR-based methods. 

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5. Functions on Symmetric Matrices and Point Clouds via Lightweight Invariant Features from Galois Theory

   https://doi.org/10.1137/24M1659972  

   Blum-Smith, Ben; Huang, Ningyuan Teresa; Cuturi, Marco; Villar, Soledad (December 2025, SIAM Journal on Applied Algebra and Geometry)

   In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by conjugation, and (2) functions on point clouds that are invariant with respect to rotations, reflections, and permutations of the points. To achieve this, we provide a construction of generically separating invariant features using ideas inspired by Galois theory. We construct 𝑂⁡(𝑛^2) invariant features derived from generators for the field of rational functions on 𝑛 ×𝑛 symmetric matrices that are invariant for joint permutations of rows and columns. We show that these invariant features can separate all distinct orbits of symmetric matrices, except for a measure zero set; such features can be used to universally approximate invariant functions on almost all weighted graphs. For point clouds in a fixed dimension, we prove that the number of invariant features can be reduced, generically without losing expressivity, to 𝑂⁡(𝑛), where 𝑛 is the number of points. We combine these invariant features with DeepSets to learn functions on symmetric matrices and point clouds with varying sizes. We empirically demonstrate the feasibility of our approach on molecule property regression and point cloud distance prediction. 

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