A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Computer aided detection of defects in FRP bridge decks using infrared thermography

The objective of this research is to develop a turn-key system that is able to interface with the FLIR ThermaCAM S60 infrared camera and automatically capture and analyze defects in infrared images of FRP bridge decks. Infrared thermography is one of the nondestructive evaluation (NDE) techniques that are being used to locate defects (debonds and delaminations) in bridge components. It is a rapid data collection and interpretation technique having high sensitivity and reliability. Analysis of infrared images by human interpretation is dependent on the users knowledge and hence introduces ambiguity in the defect detection process.;This thesis investigates the use of an automated defect detection system to locate defects in infrared images of FRP bridge decks to eliminate/reduce human intervention. Air-filled and water-filled debonds were inserted between the wearing surface and the underlying FRP deck. Also, simulated subsurface delaminations (of various sizes and thickness) were created at the flange-to-flange junction between two FRP deck modules. (Abstract shortened by UMI.)

Inspection of the integrity of surface mounted integrated circuits on a printed circuit board using vision

Machine vision technology has permeated many areas of industry, and automated inspection systems are playing increasingly important roles in many production processes. Electronic manufacturing is a good example of the integration of vision based feedback in manufacturing and the assembly of surface mount PCBs is typical of the technology involved. There are opportunities to use machine vision during different stages of the surface mount process. The problem in the inspection of solder joints on surface mount printed circuit board is much more difficult than many other inspection problems. In this thesis, an approach for inspecting surface mounted integrated circuits (SMICs) is presented. It is based on the variance of intensity values of pixels in an image. This method is able to cope with 4 kinds of soldering defects in SMICs. A set of modules for the system is proposed. The computer program which performs the image processing and analyzing has been written in C. It has been linked with a number of image processing routines from MAVIS1 to perform some image processing tasks, and the result is a compact executable module which works under MS-DOS2 3.30

Object Detection Based on Template Matching through Use of Best-So-Far ABC

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution

Computing With Hybrid Material Oscillators

The evolution of computers is driven by advances not only in computer science, but also in materials science. As the post-CMOS era approaches, research is increasingly focusing on flexible and unconventional computing systems, including the study of systems that incorporate new computational paradigms into the materials, enabling the computer and the material to be the same entity. In this dissertation, we design a coupled oscillator system based on a new hybrid material that can autonomously transduce chemical, mechanical, and electrical energy. Each material unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemo-mechanical oscillations of the BZ gels deflect the piezoelectric layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these coupled units become synchronized across the network, with the mode of synchronization depending on the polarity of the piezoelectric. Taking advantage of this synchronization behavior, we demonstrate that the network of coupled BZ-PZ oscillators can perform specific computational tasks such as pattern matching in a self-organized manner, without external electrical power sources. The results of the computational modeling show that the convergence time for stable synchronization gives a distance measure between the “stored” and “input” patterns, which are encoded by the connection and phases of BZ-PZ oscillators. In addition, we demonstrate two methods to enrich the information representation in our system. One is to employ multiple BZ-PZ oscillator networks in parallel and to process information encoded in different channels. The other is to introduce capacitors into a BZ-PZ network that modify the dynamical behavior of the systems and increase the information storage. We analyze and simulate the proposed coupled oscillator systems by using linear stability analysis and phase models and explore their potential computational capabilities. Through these studies, we establish experimentally realizable design rules for creating “materials that compute”

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Spectral imaging for high-throughput metrology of large-area nanostructure arrays

Modern high-throughput nanopatterning techniques such as nanoimprint lithography make it possible to fabricate arrays of nanostructures (features with dimensions on the 10’s to 100’s of nm scale) over large area substrates (in² to m² scale) such as Si wafers, glass sheets, and flexible roll-to-roll webs. The ability to make such large area nanostructure arrays, or “LNAs” as we will call them, gives birth to an extensive design space enabling a wide array of applications. For instance, LNAs exhibit nanophotonic properties enabling optical devices like wire-grid polarizers (WGPs), transparent conducting metal mesh grids (MMGs), color filters, perfect mirrors, and anti-reflection surfaces. LNAs can also be utilized for increasing surface area as well as generally creating large arrays of discrete features to be utilized as building blocks for electronic components in memory storage devices, sensors, and microprocessors. These unique properties make LNAs immediately attractive to certain industries such as the display and photovoltaic industries. As fabrication methods for LNAs are becoming viable, various industries are becoming interested in pursuing high-volume manufacturing of LNAs for these applications. Unfortunately, metrology methods are currently rudimentary outside of the silicon integrated circuits industry, impeding manufacturing scalability in applications such as displays and photovoltaics. Metrology is essential in the manufacturing context, because it provides invaluable feedback on the success of the fabrication process, both during new process development and large-scale production by tracking of device quality metrics, including performance and reliability metrics, and enables classification of defects that cause devices to not achieve desired quality metrics. Traditional nanometrology methods have fundamental issues which make their applicability to LNA manufacturing difficult. In particular, their low throughput is a major deal-breaker. Fortunately, the nanophotonic properties of LNAs offer a convenient basis for metrology which offers the potential to bridge the gap between the macro and nano scales. This is because the nanophotonic properties of LNAs are inherently geometry dependent, meaning that the optical effects observed from LNAs on the macroscale give direct insight into what is happening on the nanoscale. These optical properties can be characterized using spectral imaging methods such as RGB color imaging, multispectral imaging, and hyperspectral imaging. The throughput of these systems can be extremely high relative to traditional metrology approaches. For instance, a hyperspectral imaging system, when optimized, can achieve throughput of 2.6 m²/hr with 61 spectral bands (wavelength centers of 400 to 700 nm in steps of 5 nm) and a resolution of 10 x 10 µm. An RGB imaging system can achieve an even higher throughput of 15.3 m²/hr. The 10 x 10 µm lateral resolution is often adequate for display and photovoltaic applications. The high throughput makes this approach is incredibly attractive. In this dissertation, we show how spectral imaging techniques can be applied to metrology characterization tasks including defect detection and classification as well as providing a geometric measurement capability via a technique called optical critical dimension (OCD) scatterometry. In this work, we utilize exemplar manufacturing methods, namely JFIL nanoimprint lithography, to create a variety of exemplar LNAs on which we demonstrate the various metrology capabilities of spectral imaging. These LNAs include plasma etched vertical Si nanopillar arrays, metal assisted chemical etching (MACE) vertical Si nanowire arrays, WGPs, and MMGs. Each of these devices has unique manufacturing processes, and we show how the various manufacturing process steps can create a variety of different defects. Naturally, many of the defects originate in the nanoimprint process which lithographically defines the features. We show how defects like particle contamination, non-filling, residual layer thickness (RLT) variations, and adhesion failure uniquely manifest as changes in the optical signatures of the LNAs and use this principle to provide a basis for defect detection. Then, we show how image processing methods can be used to classify what types of defects have occurred over large areas such as wafer scale. Furthermore, we demonstrate that spectral imaging can be used as a geometric metrology using the OCD method, and show how hyperspectral imaging, in particular, can provide geometric measurement on wafer scale areas. The large field of view (FOV), high spatial resolution, and high speed offered by the spectral imaging approach allows for identification of a variety of interesting defect signatures that would be difficult, or nearly impossible, to observe using other metrology approaches. Finally, we discuss ongoing development of a spectral imaging system for roll-to-roll (R2R) LNA manufacturing. Construction of this system will begin in the months following this dissertation and will primarily be applied to manufacturing of WGPs and MMGs on R2R. In summary, these demonstrations are intended to serve as a demonstration of the use of spectral imaging wherever possible in LNA manufacturing. Naturally, this requires that the LNAs being manufacturing exhibit significant enough optical effects for the approach to work, but when this is the case, the advantages of the approach appear outstanding and thus have the potential to be utilized in volume manufacturing of LNAs.Mechanical Engineerin

Electrochemical modulation and restructing of planar metallic metamaterials

Nano-plasmonics as well as the majority of photonic metamaterials rely on resonances of metallic nanostructures. For many applications, it would obviously be desirable to tune these resonances. In this thesis lithographically manufactured gold resonators are presented, whose central frequency and damping properties are modulated by an electrochemical approach. The optical properties of the manufactured samples are investigated experimentally and are confirmed by numerical calculations