Defect Analysis Approach Research Articles

Accelerating defect analysis of solar cells via machine learning of the modulated transient photovoltage

Fast and non-destructive analysis of material defect is a crucial demand for semiconductor devices. Herein, we are devoted to exploring a solar-cell defect analysis method based on machine learning of the modulated transient photovoltage (m-TPV) measurement. The perturbation photovoltage generation and decay mechanism of the solar cell is firstly clarified for this study. High-throughput electrical transient simulations are further carried out to establish a database containing millions of m-TPV curves. This database is subsequently used to train an artificial neural network to correlate the m-TPV and defect properties of the perovskite solar cell. A Back Propagation neural network has been screened out and applied to provide a multiple parameter defect analysis of the cell. This analysis reveals that in a practical solar cell, compared to the defect density the charge capturing cross-section plays a more critical role in influencing the charge recombination properties. We believe this defect analysis approach will play a more important and diverse role for solar cell studies.

Nano−Micro Characterization of Defects on Silicon Surfaces: An Industrial Perspective of Metrology Challenges

The electronic device industry is known to have been driven since its early stages by a continuous technological effort of miniaturization because of the dual need of manufacturing cost reduction through integration and offers to the user market of smaller electronic applications, more efficient, versatile, and differentiable. Silicon wafer manufacturers have been forced to provide larger size silicon wafers. 300 mm diameter is nowadays covering more than 50% of the total market, with dimension and density of residual defects moving from micrometers to nanometers, from ppt to ppq, or even less. In this context, the proper choice of measurement technique and setup which can guarantee measurement reliability to detect a given defect and provide information of its distribution inside the wafer is mandatory. It has to cope the need to control material quality in a range more frequently close to the measurement detection limit and improve the associated manufacturing process capabilities. Herein, a few examples in the area of crystallographic/morphological defects and chemical impurities on silicon wafers are proposed, reviewing the defect analysis approach inside a manufacturing environment. The discussion also involves the final product certification data reporting which tends to convey in a very synthetic format the whole measurement and control process.

Analyzing wafer leveling data from high-volume manufacturing to identify the sources of inline defectivity

Sampling-limited inline defect inspections may fall short in the timely detection of new defects or small baseline populations, especially when the defects have unique spatial orientations. In such cases, it may be beneficial to also consider fault detection and classification signals from unit process modules. Lithography scanners determine the optimal focus position for a wafer by a process called leveling. This work uses a defect analysis approach to examine focus spot data, a litho tool level signal extracted from wafer leveling, and to isolate the sources of inline defectivity from four consecutive front-end-of-line litho steps in a high-volume manufacturing fab. The scope is broadened to examine all litho layers from the same technology node that process on the same tool platform. This work highlights the immense potential of mining focus spot data as a powerful complement to inline defect monitoring.

Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

An Intelligent and Confident System for Automatic Surface Defect Quantification in 3D

Automatic surface defect inspection within mass production of high-precision components is growing in demand and requires better measurement and automated analysis systems. Many manufacturing industries may reject manufactured parts that exhibit even minor defects, because a defect might result in an operational failure at a later stage. Defect quantification (depth, area and volume) is a key element in quality assurance in order to determine the pass or failure criterion of manufactured parts. Existing human visual analysis of surface defects is qualitative and subjective to varying interpretation. Non-contact and three dimensional (3D) analyses should provide a robust and systematic quantitative approach for defect analysis. Various 3D measuring instruments generate point cloud data as an output, although they work on different physical principles. Instrument’s native software processing of point cloud data is often subject to issues of repeatability and may be non-traceable causing significant concern with data confidence.This work reports the development of novel traceable surface defect artefacts produced using the Rockwell hardness test equipment on flat metal plate, and the development of a novel, traceable, repeatable, mathematical solution for automatic defect detection and quantification in 3D. Moreover, in order to build-up the confidence in automatic defect analysis system and generated data, mathematical simulated defect artefacts (soft-artefact) have been created. This is then extended to a surface defect on a piston crown that is measured and quantified using a parallel optical coherence tomography instrument integrated with 6 axis robot. The results show that surface defect quantification using implemented solution is efficient, robust and more repeatable than current alternative approaches.

GA‐based multi-level association rule mining approach for defect analysis in the construction industry

Abstract In construction industry, work defects yield time and cost overruns of construction projects and also cause disputes between project participants during construction and operation phases. To date, there hasn't yet been an adequate analytical model to extract useful information from the database of construction defects. The information represented in the form of association rules could enhance quality management via defect prediction and causation analysis. This paper proposes a Genetic Algorithm (GA)-based approach that incorporates the concept hierarchy of construction defects to discover multi-level patterns of defects from the database of defects in the Chinese construction industry during 2000 to 2010. First, the domain knowledge of construction defect is incorporated into a concept hierarchy to adjust mining items at different levels according to the data sparseness and the interestingness of a rule. Second, a GA-based approach is proposed to generate interesting association rules without specified threshold of minimum confidence, taking advantage of the searching capability of GA. Finally, the redundant rules in the mining results are pruned by post-processing method. A test case is selected to demonstrate the feasibility and applicability of the proposed approach within the problem domain. It is concluded that the proposed method provided an effective tool to discover useful knowledge hidden in historical defect cases. The discovered knowledge indicating relationships between defects and defect causes enables project managers to make strategies for estimating and reducing defects.

Defect analysis in mission-critical software systems: a detailed investigation

The practice of defect analysis is recognized as an essential task for software process measurement, yet its effective application in the industrial development of large-scale software systems raises several challenges. We report the results of a study conducted at SELEX ES – a large system integrator leader in the market of software-intensive mission-critical systems. The article describes the defect analysis approach that we tailored to evaluate the software development process with respect to the quality of produced software and its relation with the required effort. Three key phases of the process were addressed, regarding the software implementation, the testing phase and the prerelease defect fixing activity, over a set of six computer software configuration items developed from 2009 to 2012 for the naval and maritime domain product line. The analysis highlighted efficiency bottlenecks in each of the monitored phases, providing company engineers with insights about room for process improvement. The implemented approach, the observed phenomena and the inferred conclusions are of support to practitioners coping with systems, development models and industrial environments similar to the considered one. Copyright © 2014 John Wiley & Sons, Ltd.

Detection of Spatial Defect Patterns Generated in Semiconductor Fabrication Processes

Spatial defect patterns generated during integrated circuit (IC) manufacturing processes contain information about potential problems in the processes. The detection of these defect patterns is crucial to improve yield and reliability in IC manufacturing. This paper proposes a multistep defect analysis approach that provides clustering results with different levels of accuracy. A defect denoising step, based on the K th nearest-neighbor noise removal technique, determines the existence of any clustered local defects on a wafer. If local defects exist, the denoising step separates local defects from global defects. A defect clustering step applies a similarity-based clustering technique to group the local defects into clusters according to their spatial locations. A pattern identification step identifies the pattern for each of the local defect clusters (i.e., linear, curvilinear, amorphous, or ring-shaped patterns) via various model selection criteria. Finally, a fine tuning step is applied in order to improve the accuracy of the clustering performance. The fine tuning step is based on model-based clustering with a fixed number of clusters and known patterns for each cluster. The results of both simulated and real wafer map data demonstrate the potential of our approach, both in terms of computational speed and detection accuracy, for analyzing general defect patterns generated during the IC fabrication process.