Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology

• URL: http://arxiv.org/abs/2001.09612v1
• Date: Mon, 27 Jan 2020 07:37:47 GMT
• Title: Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology
• Abstract summary: Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) During reflow process, once deposited solder pastes start melting, electronic components move in a direction that achieve their highest symmetry. In this study, two noticeable machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR) are proposed as a prediction technique.
• Score: 2.6168876987285308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) and are permanently attached on PCB with the aim of reflow soldering process. During reflow process, once deposited solder pastes start melting, electronic components move in a direction that achieve their highest symmetry. This motion is known as self-alignment since can correct potential mounting misalignment. In this study, two noticeable machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR) are proposed as a prediction technique to (1) diagnose the relation among component self-alignment, deposited solder paste status and placement machining parameters, (2) predict the final component position on PCB in x, y, and rotational directions before entering in the reflow process. Based on the prediction result, a non-linear optimization model (NLP) is developed to optimize placement parameters at initial stage. Resultantly, RFR outperforms in terms of prediction model fitness and error. The optimization model is run for 6 samples in which the minimum Euclidean distance from component position after reflow process from ideal position (i.e., the center of pads) is outlined as 25.57 ({\mu}m) regarding defined boundaries in model.

Related papers

• Stokes-Informed Diffusion for Robust Linear Polarization Estimation [49.459667092743445]
  GenPolar is a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation.<n>It achieves state-of-the-art performance in both DoLP fidelity and AoP stability.<n>These improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
  arXiv  Detail & Related papers  (2026-07-23T11:56:15Z)
• Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
  We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
  arXiv  Detail & Related papers  (2026-07-13T14:03:26Z)
• Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion [43.450233708268684]
  Co4ICF is a co-evolving framework that couples a physics-informed surrogate with a PPO-based pulse.<n>In the 1D MULTI environment, Co4ICF achieves 146.1% normalized yield based on current laser design baseline.<n>We release a large-scale MULTI-IFE simulation dataset to support future benchmarking.
  arXiv  Detail & Related papers  (2026-07-11T15:43:27Z)
• Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers [68.9946633641494]
  Flow matching and Schrdinger bridges define probability, yet their inference grids are usually or inherited from one-endpoint diffusion.<n>We derive a conditional-marginal entropy-rate objective for bridge-aware discretization, separating endpoint-conditioned bridge geometry from marginal flow evolution.<n>On EDM/CIFAR-10, the entropic time-discretization gives the best tested five-step FID (186.3 pm 4.0 versus 200.5 pm 2.9 for linear and 238.0 pm 5.3 for cosine)
  arXiv  Detail & Related papers  (2026-05-15T16:11:10Z)
• FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition [10.969341855852276]
  Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part.<n>This paper introduces FLARE, a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters.<n> FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space.
  arXiv  Detail & Related papers  (2026-04-17T19:11:27Z)
• Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading [86.6550968435969]
  Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks.<n>We benchmark out-of-distribution transfer on two discontinuity-dominated regimes in which shocks, evolving interfaces, and fracture produce highly non-smooth fields.<n>We evaluate two open-source PDE foundation models, POSEIDON and MORPH, and compare fine-tuning from pretrained weights against training from scratch across training-set sizes to quantify sample efficiency under distribution shift.
  arXiv  Detail & Related papers  (2026-03-04T18:19:35Z)
• Parallel Diffusion Solver via Residual Dirichlet Policy Optimization [88.7827307535107]
  Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature.<n>Existing solver-based acceleration methods often face significant image quality degradation under a low-dimensional budget.<n>We propose the Ensemble Parallel Direction solver (dubbed as EPD-EPr), a novel ODE solver that mitigates these errors by incorporating multiple gradient parallel evaluations in each step.
  arXiv  Detail & Related papers  (2025-12-28T05:48:55Z)
• Calibrated Principal Component Regression [5.890015449615021]
  Principal Component Regression (PCR) incurs truncation bias when the true regression vector has mass outside the retained principal components (PC)<n>We propose Calibrated Principal Component Regression (CPCR), which first learns a low-variance prior in the PC subspace and then calibrates the model in the original feature space via a centered Tikhonov step.
  arXiv  Detail & Related papers  (2025-10-21T19:01:42Z)
• Kernel-Adaptive PI-ELMs for Forward and Inverse Problems in PDEs with Sharp Gradients [0.0]
  This paper introduces the Kernel Adaptive Physics-Informed Extreme Learning Machine (KAPI-ELM)<n>It is designed to solve both forward and inverse Partial Differential Equation (PDE) problems involving localized sharp gradients.<n>KAPI-ELM achieves state-of-the-art accuracy in both forward and inverse settings.
  arXiv  Detail & Related papers  (2025-07-14T13:03:53Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs) The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization [13.409482818102878]
  Galibr is a fully automatic LiDAR-camera extrinsic calibration tool designed for ground vehicle platforms in any natural setting. The method utilizes the ground planes and edge information from both LiDAR and camera inputs, streamlining the calibration process. Our approach significantly enhances calibration performance, primarily attributed to our novel initial pose estimation method.
  arXiv  Detail & Related papers  (2024-06-14T08:25:10Z)
• Intrinsic Voltage Offsets in Memcapacitive Bio-Membranes Enable High-Performance Physical Reservoir Computing [0.0]
  Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Here, we introduce a novel memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and non-monotonic input-state correlations. Our approach and unprecedented performance are a major milestone towards high-performance full in-materia PRCs.
  arXiv  Detail & Related papers  (2024-04-27T05:47:38Z)
• DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration [73.37538551605712]
  Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds. We propose formulating PCR as a denoising diffusion probabilistic process, mapping noisy transformations to the ground truth. Our experiments showcase the effectiveness of our DiffusionPCR, yielding state-of-the-art registration recall rates (95.3%/81.6%) on 3D and 3DLoMatch.
  arXiv  Detail & Related papers  (2023-12-05T18:59:41Z)
• Predicting Surface Texture in Steel Manufacturing at Speed [81.90215579427463]
  Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements. We propose the use of machine learning to improve accuracy of the transformation from inline laser reflection measurements to a prediction of surface properties.
  arXiv  Detail & Related papers  (2023-01-20T12:11:03Z)
• A Data-driven Reduced Order Modeling Approach Applied In Context Of Numerical Analysis And Optimization Of Plastic Profile Extrusion [0.0]
  We develop a data-driven reduced order model (ROM) for the purpose of predicting temperature distributions within the extruded profiles inside the calibration unit. Therein, the ROM functions as a first step to our overall goal of prediction based process control in order to avoid undesired warpage and damages of the final product.
  arXiv  Detail & Related papers  (2022-09-07T13:12:28Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow [64.81110234990888]
  Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size (HDLSS) setting, one may prefer modified principal components, with penalized loadings. We propose Approximated Gradient Flow (AgFlow) as a fast model selection method for penalized PCA.
  arXiv  Detail & Related papers  (2021-10-07T08:57:46Z)
• Learning to predict metal deformations in hot-rolling processes [59.00006390882099]
  Hot-rolling is a metal forming process that produces a cross-section from an input through a sequence of deformations. In current practice, the rolling sequence and the geometry of their rolls are needed to achieve a given cross-section. We propose a supervised learning approach to predict a given by a set of rolls with given geometry.
  arXiv  Detail & Related papers  (2020-07-22T13:33:44Z)
• Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline [54.73337667795997]
  Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs.
  arXiv  Detail & Related papers  (2020-07-03T23:44:21Z)
• A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics [3.8698051494433043]
  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.
  arXiv  Detail & Related papers  (2020-02-25T15:54:33Z)
• Data-Driven Prediction Model of Components Shift during Reflow Process in Surface Mount Technology [1.4680035572775534]
  In surface mount technology (SMT), mounted components on soldered pads are subject to move during reflow process. This capability is known as self-alignment and is the result of fluid dynamic behaviour of molten solder paste. The aim of this study is to develop a machine learning model that predicts the components movement during reflow in x and y-directions.
  arXiv  Detail & Related papers  (2020-01-27T08:00:23Z)

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.