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    <id>5830</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>28</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data</title>
    <abstract language="eng">Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. &#13;
&#13;
To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. &#13;
&#13;
The research focuses on crack propagation in ceramic-based high-power LED packages used in automotive lighting systems, incorporating variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates with validated FEA models. Comparative analysis shows that PointNet significantly outperforms 3D CNNs, achieving an exceptionally high correlation with experimental data (R² = 99.99%). This AI-driven automated feature extraction and lifetime prediction approach marks a major advancement over traditional FEA-based methods, offering superior accuracy, reliability, and scalability for predicting solder joint reliability in microelectronics.</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-58308</identifier>
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    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5830/AI-Driven_Mohd_Schmid_Elger.pdf</file>
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  <doc>
    <id>6241</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
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    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-10-02</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric</title>
    <abstract language="eng">The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.</abstract>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6241/Quantitative_Kernel_Estimation.pdf</file>
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