Reference-free detection of semiconductor assembly defect

This paper aims at developing a novel defect detection algorithm for the semiconductor assembly process by image analysis of a single captured image, without reference to another image during inspection. The integrated circuit (IC) pattern is usually periodic and regular. Therefore, we can implement a classification scheme whereby the regular pattern in the die image is classified as the acceptable circuit pattern and the die defect can be modeled as irregularity on the image. The detection of irregularity in image is thus equivalent to the detection of die defect. We propose a method where the defect detection algorithm first segments the die image into different regions according to the circuit pattern by a set of morphological segmentations with different structuring element sizes. Then, a feature vector, which consists of many image attributes, is calculated for each segmented region. Lastly, the defective region is extracted by the feature vector classification. © 2005 SPIE and IS&T.published_or_final_versio

Automatic Defect Detection System For Leadframe Inspection

Defect detection and classification are important for both product quality assurance and process improvement in the maimfacturing industry. Machine vision systems offer several beneficial. features such as consistency, accuracy and round the clock repeatability. This thesis presents the results of the development and implementation of such a machine vision system to automate the inspection of leadframes. Pengesanan dan mengklasifikasikan kecacatan adalah penting untuk memastikan kualiti produk dan meningkatkan kebolehan sesuatu proses dalam industri pembuatan. Sistem penglihatan mesin menawarkan beberapa kelebihan dalam perkara seperti konsisten, kejituan dan pemeriksaan berterusan. Disertasi ini mempersembahkan keputusan dalam membangun dan implementasi sistem penglihatan mesin untuk pemeriksaan secara bagi 'leadframe'

BagStack Classification for Data Imbalance Problems with Application to Defect Detection and Labeling in Semiconductor Units

abstract: Despite the fact that machine learning supports the development of computer vision applications by shortening the development cycle, finding a general learning algorithm that solves a wide range of applications is still bounded by the ”no free lunch theorem”. The search for the right algorithm to solve a specific problem is driven by the problem itself, the data availability and many other requirements. Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging. In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

A three-dimensional imaging system for surface profilometry of moving objects

Non-contact optical imaging system design and the corresponding surface profilometry algorithm are critical components in various metrology applications, such as surface inspection of semiconductor components on the production line. For such challenging industrial applications, the most important considerations are often automation, precision and speed of the inspection. In this work, we propose a mathematical framework and a dynamic phase-shift algorithm (D-PSA) for a dense surface profilometry of moving objects. We also present a fringe pattern projection system with projector and camera arrays, with an aim to reduce the undesirable effects such as the uneven illumination and the perspective geometry effect on the reconstructed surface using a large field-of-view inspection system. This system is then applied to the inspection of the surface of moving printed circuit boards along a conveyor belt. Experimental results show that our approach can reconstruct the object surface effectively and efficiently. © 2013 IEEE.published_or_final_versio

Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review

A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

An INSPECT Measurement System for Moving Objects

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Structured-light based sensing using a single fixed fringe grating: Fringe boundary detection and 3-D reconstruction

Advanced electronic manufacturing requires the 3-D inspection of very small surfaces like the solder bumps on wafers for direct die-to-die bonding. Yet the microscopic size and highly specular and textureless nature of the surfaces make the task difficult. It is also demanded that the size of the entire inspection system be small so as to minimize restraint on the operation of the various moving parts involved in the manufacturing process. In this paper, we describe a new 3-D reconstruction mechanism for the task. The mechanism is based upon the well-known concept of structured-light projection, but adapted to a new configuration that owns a particularly small system size and operates in a different manner. Unlike the traditional mechanisms which involve an array of light sources that occupy a rather extended physical space, the proposed mechanism consists of only a single light source plus a binary grating for projecting binary pattern. To allow the projection at each position of the inspected surface to vary and form distinct binary code, the binary grating is shifted in space. In every shift, a separate image of the illuminated surface is taken. With the use of pattern projection, and of discrete coding instead of analog coding in the projection, issues like texture-absence, image saturation, and image noise of the inspected surfaces are much lessened. Experimental results on a variety of objects are presented to illustrate the effectiveness of this mechanism. © 2008 IEEE.published_or_final_versio

An illumination-invariant phase-shifting algorithm for three-dimensional profilometry

Image Processing: Machine Vision Applications V, Burlingame, California, USA, 22 January, 2012Uneven illumination is a common problem in real optical systems for machine vision applications, and it contributes significant errors when using phase-shifting algorithms (PSA) to reconstruct the surface of a moving object. Here, we propose an illumination-reflectivity-focus (IRF) model to characterize this uneven illumination effect on phase-measuring profilometry. With this model, we separate the illumination factor effectively, and then formulate the phase reconstruction as an optimization problem. To simplify the optimization process, we calibrate the uneven illumination distribution beforehand, and then use the calibrated illumination information during surface profilometry. After calibration, the degrees of freedom are reduced. Accordingly, we develop a novel illumination-invariant phase-shifting algorithm (II-PSA) to reconstruct the surface of a moving object under an uneven illumination environment. Experimental results show that the proposed algorithm can improve the reconstruction quality both visually and numerically. Therefore, using this IRF model and the corresponding II-PSA, not only can we handle uneven illumination in a real optical system with a large field of view (FOV), but we also develop a robust and efficient method for reconstructing the surface of a moving object. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).link_to_subscribed_fulltextpublished_or_final_versio