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.

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

Development of decentralized solutions for managing individual products in manufacturing and postmanufacturing applications makes it possible to embed automation in the product flow, which is more efficient compared to the conventional approach based on automating individual unit processes. The decentralized approach can be enabled using manufacturing objects that are capable of negotiating with their environment, observing it, making decisions according to these observations, and sharing information on their status and needs. For these purposes, automatic identification (auto-ID) technology is required. Automatic identification involves techniques for automatically identifying things by using tokens with individual codes, collecting information on them, and transferring that information automatically into computer systems. In general, not only techniques such as barcode, optical character recognition (OCR), and Radio Frequency Identification (RFID) are associated with auto-ID but also other techniques that are less used in manufacturing can be highlighted. The present study seeks to map the existing techniques for automatic identification of objects and to determine their most potential targets of use in manufacturing.

Toward decentralized intelligence in manufacturing: recent trends in automatic identification of things

An on-line model development and quality control methodology is presented for manufacturers in the process industries with the goal of enabling automated quality assurance. Given appropriate process instrumentation, the methodology starts with the characterization of statistical variation for the process while operating in steady state. Significant process conditions are then perturbed by six standard deviations to bound the expected long-term process variation including lot-to-lot variability of feedstock materials. If the process is found to be robust, acquired process data is used to model the process behavior using principle components analysis (PCA). The PCA model is then used to accept and reject manufactured parts given real-time process data. This methodology is applied to an instrumented injection molding process that is subjected to 12 common process faults. The results indicate that the methodology was able to detect every one of 33 defective molding cycles caused by eight of the imposed faults as well as two additional faults that did not result in observable defective products. The quality controller did not detect the two remaining imposed process faults that did not produce defects and also rejected three molding cycles (2% of the total) that appeared to produce acceptable products.

A model-based methodology for on-line quality control.

A Comparison of South East Asian Face Emotion Classification Based on Optimized Ellipse Data Using Clustering Technique

In this paper, lip features are applied to classify the human emotion using a set of irregular ellipse fitting equations using Genetic algorithm. As Japanese, is considered in this study. All six universally accepted emotions are considered for classifications. Lip is usually considered as one of the features for recognizing the emotion. In this work, three feature extraction methods are proposed and their respective performances are compared for determining the feature of the lips. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips. GA is adopted to optimize such irregular ellipse characteristics of the lip features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotion. This has given reasonably successful emotion classifications for Japanese subject.

Japanese Face Emotions Classification Using LIP Features

In this paper, a real time industrial machine vision system incorporating optical character recognition (OCR) is employed to inspect markings on integrated circuit (IC) chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instruments is used in the investigation. The IC chip markings are laser printed. This inspection system tests whether the laser printed marking on IC chips is proper. The inspection has to identify print errors such as illegible characters, missing characters and upside down printing. The vision inspection of the printed markings on the IC chip is carried out in three phases, namely, image preprocessing, feature extraction and classification. The MATLAB platform and its toolboxes are used for designing the inspection processing technique. Speed of the marking inspection is mostly dependent on the effectiveness of the feature extraction technique. The performances of four feature extraction techniques are compared in terms of their respective speed. The feature extracted data are used in a neural network for classifying the marking errors. A suggestion to optimize the number of input neurons of the neural network for a fast classification is also presented.

A real time marking inspection scheme for semiconductor industries

In this paper, an Industrial machine vision system incorporating Optical Character Recognition (OCR) is employed to inspect the marking on the Integrated Circuit (IC) Chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instrument is used in the investigation. The IC chips are laser printed. This inspection system ensures whether the laser printed marking on IC chips are proper. One of the artificial intelligence components the neural network, is used for inspection. The inspections are carried out to find the print errors such as illegible character, missing characters and up side down printing. The vision inspection of the printed markings on the IC chip are carried out in three phases namely image preprocessing, feature extraction and classification. MATLAB platform and its toolboxes are used for designing the inspection processing technique. Neural network is used as a classifier to detect the defectively marked IC chips coming from the manufacturing line. In neural network, feature extracted from moments and projection profile are used for inspection. Both feature extraction methods are compared in terms of marking inspection time.

Paulraj Pandian

Papers

A real time marking inspection scheme for semiconductor industries

Classification of the marking on integrated circuit chips based on moments and projection profile - a comparison