A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics

• URL: http://arxiv.org/abs/2002.10986v1
• Date: Tue, 25 Feb 2020 15:54:33 GMT
• Title: A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics
• Authors: Nikolaos Dimitriou, Lampros Leontaris, Thanasis Vafeiadis, Dimosthenis Ioannidis, Tracy Wotherspoon, Gregory Tinker, Dimitrios Tzovaras
• Abstract summary: We propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters. We validate our framework on a microelectronics use-case using the recently published PCB scans dataset.
• Score: 3.8698051494433043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prediction of upcoming events in industrial processes has been a long-standing research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published PCB scans dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

Related papers

• OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Prediction [5.285847977231642]
  3D semantic occupancy prediction is crucial for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features. We propose OccLoff, a framework that Learns to optimize Feature Fusion for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-11-06T06:34:27Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks [2.147634833794939]
  Crud-Induced Power Shift (CIPS) is an operational challenge in Pressurized Water Reactors. This work proposes a top-down approach to predict CIPS instances on an assembly level with reactor-specific calibration built-in.
  arXiv  Detail & Related papers  (2024-06-27T15:04:24Z)
• Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction [1.6574413179773761]
  In engineering design, surrogate models are widely employed to replace computationally expensive simulations. We propose a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based surrogate model. Our framework determines the optimal size of mesh elements through Bayesian optimization, resulting in a high-accuracy surrogate model.
  arXiv  Detail & Related papers  (2024-06-04T06:27:48Z)
• Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data [2.306144660547256]
  We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels.
  arXiv  Detail & Related papers  (2023-09-08T00:26:44Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data [0.0]
  We present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation.
  arXiv  Detail & Related papers  (2022-03-17T09:54:22Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.