Industry 4.0: Mining Physical Defects in Production of Surface-Mount Devices

With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices

3-D measurement of solder paste using two-step phase shift profilometry

A two-step phase shift profilometry method (2-step PSP) with prefiltering and postfiltering stages is proposed to reconstruct the 3-D profile of solder paste. Two sinusoidal patterns which are π-out-of-phase are used in the 3-D reconstruction. The new method uses only two fringe patterns rather than four as the four-step phase shift profilometry (4-step PSP). In Fourier transform profilometry (FTP), a bandpass filter is required to extract the fundamental spectrum from the background and higher order harmonics due to camera noise and imperfectness of the pattern projector. By using two π-out-of-phase sinusoidal fringe patterns, the background term can be eliminated directly by taking the average of the two fringe patterns. The fringe pattern which is close to its ideal form can also be recovered from the averaging process. Prefiltering is utilized in filtering raw images to remove noise causing higher order harmonics. Hilbert transform is then used to obtain the in-quadrate component of the processed fringe pattern. Postfiltering is applied for reconstructing an appropriate 3-D profile. © 2008 IEEE.published_or_final_versio

Height inspection of wafer bumps without explicit 3D reconstruction.

by Dong, Mei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 83-90).Abstracts in English and Chinese.INTRODUCTION --- p.1Chapter 1.1 --- Bump Height Inspection --- p.1Chapter 1.2 --- Our Height Inspection System --- p.2Chapter 1.3 --- Thesis Outline --- p.3BACKGROUND --- p.5Chapter 2.1 --- Wafer Bumps --- p.5Chapter 2.2 --- Common Defects of Wafer Bumps --- p.7Chapter 2.3 --- Traditional Methods for Bump Inspection --- p.11BIPLANAR DISPARITY METHOD --- p.22Chapter 3.1 --- Problem Nature --- p.22Chapter 3.2 --- System Overview --- p.25Chapter 3.3 --- Biplanar Disparity Matrix D --- p.30Chapter 3.4 --- Planar Homography --- p.36Chapter 3.4.1 --- Planar Homography --- p.36Chapter 3.4.2 --- Homography Estimation --- p.39Chapter 3.5 --- Harris Corner Detector --- p.45Chapter 3.6 --- Experiments --- p.47Chapter 3.6.1 --- Synthetic Experiments --- p.47Chapter 3.6.2 --- Real image experiment --- p.52Chapter 3.7 --- Conclusion and problems --- p.61PARAPLANAR DISPARITY METHOD --- p.62Chapter 4.1 --- The Parallel Constraint --- p.63Chapter 4.2 --- Homography estimation --- p.66Chapter 4.3. --- Experiment: --- p.69Chapter 4.3.1 --- Synthetic Experiment: --- p.69Chapter 4.3.2 --- Real Image Experiment: --- p.74CONCLUSION AND FUTURE WORK --- p.80Chapter 5.1 --- Summary of the contributions --- p.80Chapter 5.2 --- Future Work --- p.81Publication related to this work: --- p.83BIBLIOGRAPHY --- p.8

3D inspection of wafer bump quality without explicit 3D reconstruction.

Zhao Yang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 87-95).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives of the Thesis --- p.1Chapter 1.2 --- Wafer bumping inspection by using Biplanar Disparity approach --- p.2Chapter 1.3 --- Thesis Outline --- p.4Chapter 2 --- Background --- p.5Chapter 2.1 --- What is wafer bump? --- p.5Chapter 2.1.1 --- Common defects of wafer bump --- p.6Chapter 2.1.2 --- Literature review on exist wafer bump inspection method --- p.11Chapter 3 --- Model 1: the one camera model-Homography approach --- p.21Chapter 3.1 --- The introduction of the theoretical base of model 1 --- p.21Chapter 3.1.1 --- The objective of model 1 --- p.21Chapter 3.1.2 --- Desires --- p.22Chapter 3.1.3 --- Some background knowledge on Homography --- p.22Chapter 3.2 --- "Model 1- ""Pseudo Homography"" Approach" --- p.24Chapter 3.2.1 --- The description of the configuration of model 1 --- p.24Chapter 3.2.2 --- The condition of pseudo Homography --- p.25Chapter 3.2.3 --- The formation of pseudo Homgraphy H --- p.26Chapter 3.3 --- Methodology of treatment of the answer set --- p.32Chapter 3.3.1 --- Singular Value Decomposition-SVD --- p.32Chapter 3.3.2 --- The Robust Estimation --- p.33Chapter 3.3.3 --- Some experimental results by using manmade Ping Pang balls to test SVD[31] and Robust Estimation [24] --- p.35Chapter 3.3.4 --- the measurement of the Homography matrix answer set --- p.37Chapter 3.4 --- Preliminary experiment about model 1 --- p.43Chapter 3.5 --- Problems unsolved --- p.47Chapter 4 --- Model 2: the two camera model-Biplanar Disparity approach --- p.48Chapter 4.1 --- Theoretical Background --- p.48Chapter 4.1.1 --- the linearization of Homography matrix changes --- p.49Chapter 4.1.2 --- Problem Nature --- p.51Chapter 4.1.3 --- Imaging system setup --- p.52Chapter 4.1.4 --- Camera Calibration[13] --- p.52Chapter 4.2 --- Methodology --- p.54Chapter 4.2.1 --- Invariance measure --- p.54Chapter 4.2.2 --- The Geometric meaning of the Biplanar Disparity matrix --- p.58Chapter 4.3 --- RANSAC-Random Sample Consensus --- p.64Chapter 4.3.1 --- finding Homography matrix by using RANSAC[72] [35] --- p.64Chapter 4.3.2 --- finding Fundamental matrix by using RANSAC[73] [34] --- p.65Chapter 4.4 --- Harris Corner detection --- p.65Chapter 5 --- Simulation and experimental results --- p.67Chapter 5.1 --- Simulation experiments --- p.67Chapter 5.1.1 --- Preliminary experiments --- p.67Chapter 5.1.2 --- Specification for the synthetic data system --- p.71Chapter 5.1.3 --- Allowed error in the experiment --- p.71Chapter 5.2 --- Real images experiments --- p.72Chapter 5.2.1 --- Experiment instrument --- p.72Chapter 5.2.2 --- The Inspection Procedure --- p.74Chapter 5.2.3 --- Images grabbed under above system --- p.75Chapter 5.2.4 --- Experimental Results --- p.81Chapter 6 --- CONCLUSION AND FUTURE WORKS --- p.83Chapter 6.1 --- Summary on the contribution of my work --- p.83Chapter 6.2 --- Some Weakness of The Method --- p.84Chapter 6.3 --- Future Works and Further Development --- p.84Chapter 6.3.1 --- About the synthetic experiment --- p.84Chapter 6.3.2 --- About the real image experiment --- p.85Bibliography --- p.8