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The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.

This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.

A case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.

The proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

The purpose of this paper is to develop an yield-based process capability index (PCI), C _py , to overcome the shortcomings of existing PCIs that limit their use and lead to inaccurate measures of quality conformance under a variety of common conditions.

–C _py is developed conceptually to flexibly and accurately reflect conformance and then used to numerically measure inaccuracies of C _pk .

–C _py overcomes many of the problems associated with existing PCIs, including C _pk . The degree of process distribution non-normality, level of quality (the sigma level), and whether the process is centered or shifted left or right affect the direction and size of process capability error produced by C _pk . The accuracy of C _pk can be greatly affected by process data that deviate even slightly from normality.

–C _py offers numerous advantages compared to existing PCIs. It accurately reflects process conformance regardless of the process distribution. It is applicable even if the process has multiple characteristics and with both variable and attribute data. Its calculation is relatively simple and the necessary data for it are likely already captured by most organizations.

The main contributions are the development of a new PCI, C _py ; a conceptual analysis of its advantages; and a numerical analysis of the improved accuracy of C _py as compared to C _pk for shifted and non-shifted process means for normal, nearly normal, and highly non-normal distributions over a range of process variability levels.

Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan‐do‐check‐act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma (6 σ) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

The purpose of this paper is to illustrate how Six Sigma methodology has been applied in a small-scale foundry industry to improve the overall first-pass material yield and quality, with a view to the product and the process.

The researchers have reported this paper based on a case study carried out in industry using the Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) and its application in improving the manufacturing process of a foundry shop.

Identified root causes are validated and countermeasures are implemented for improvement. As a result of this case study, the overall first-pass yield of the sand casting process is improved to 78.88% from the previous yield of 67%. For product-specific case, yield is improved by 18% through the improved gating system design. Sigma level of the process is improved to 3.08 from baseline 2.21. Key lessons learned from this case study are mentioned in the current study.

This case study provides a standard road map and motivates small-scale foundry industries to implement Six Sigma methodology for productivity improvement, especially in jobbing foundry. The presented paper is based on a single case study, and the results are limited to the company only. Also, one of the reasons for low process yield is slag creation, which is not covered here, as it is a concern of the material quality supplied by the vendor. However, the approach of this paper is generic for learning perspective.

This case study provides a standard road map and motivates small-scale foundry industries to implement Six Sigma methodology for productivity improvement, especially in jobbing foundry. Through the effective application of Six Sigma quality initiative, how a quantum jump in financial aspect could be gain, has been demonstrated.

This research study showcases step-by-step implementation of Six Sigma-DMAIC methodology at a small-scale foundry industry. This paper could serve as a unique roadmap for practitioners and academicians to improve the material productivity of the foundry industry both ways, product and process.

The purpose of this paper is to identify, study and quantify the effects of lighting on yield and productivity in manual electronics assembly (MEA) and inspection as a limiting work design criterion. The study also examines the potential interactions among lighting option, workers' age, and years of experience.

A three‐factor full factorial experiment is adopted to statistically evaluate the independent variables (process yield and assembly time) versus randomly selected levels of three factors: type of light (low pressure sodium, mercury vapor lamps, and metal halide lamps measured in foot‐candle luminaries), operator age, and years on the job. A residual analysis is also conducted to complement and corroborate the ANOVA findings.

The study finds that metal halide lamps, based on the ANSI recommended ranges of 186‐464 foot‐candles, lead to significant increases in labor productivity and through‐put, irrespective of operators' age and years of experience. Although these lamps have a significantly shorter life span than that of low‐pressure sodium and mercury vapor lamps, the realized benefits far exceed the incremental cost of illumination devices. The results indicate that a modest capital investment is able to generate solid improvements in yield and processing time in a typical MEA environment.

The relations between productivity and lighting intensity and type have never been studied in the area of MEA. This empirical study uncovers the effects through a systematic experimentation of this essential relationship in a typical MEA environment. The findings, which can be generalized to other facilities, are validated by an array of statistical procedures and proved to be significant. The paper contributes useful knowledge to the fields of engineering management and facility design.

The purpose of this paper is to improve the yield of a particular model of a car windshield, as the organization faces losses due to poor performance and rejection.

The Six Sigma DMAIC (define, measure, analyze, improve and control) methodology is used to reduce variation and defects in the process. It is a methodology based on data-driven and fact-based analysis to find out the root cause of the problem with the help of statistical analysis. A worst performing model is selected as a case study through the scoping tree. The preprocess, printing, bending and layup process defects are reduced by analyzing the potential causes and hypothesis testing.

This paper describes Six Sigma methodology in a glass manufacturing industry in India for automotive applications. The overall yield of a car windshield achieved 93.57 percent against the historical yield of 88.4 percent, resulting in saving 50 lacs per annum. Due to no rework or repairing in the glass, low first-time yield causes major losses. Process improvement through focused cross-functional team reduces variation in the process. Six Sigma improves profitability and reduces defects in the automotive glass manufacturing process.

This case study is applied in automotive glass manufacturing industries. For service and healthcare industries, a similar type of study can be performed. Further research on the common type of processor industry would be valuable.

The case study can be used as a problem-solving methodology in manufacturing and service industries. The tools and techniques can be used in other manufacturing processes also. This paper is useful for industries, researchers and academics for understanding Six Sigma methodology and its practical implementation.

This case study is an attempt to solve automobile glass manufacturing problems through DMAIC approach. The paper is a real case study showing benefits of Six Sigma implementation in the manufacturing industry and saving an annual cost of 50 lacs due to rejections in the process.

An innovative embedded chip MCM technology is being developed to address the packaging needs of the high volume, non‐military electronics industries. This development has evolved out of the GE High Density Interconnect (HDI) embedded chip MCM technology that was aimed at very high performance electronics in harsh military environments. In the HDI process, multiple bare chips are placed into cavities formed in a ceramic substrate and interconnected using an overlay polymer film, thin film metallisation and laser formed vias. Multiple levels of fine line (20 to 40 microns) interconnections and reference planes are used to form the circuit. In this new process, a plastic encapsulated substrate is formed by moulding a polymer resin around the bare die after placement on to a flat polymer film pre‐coated with an adhesive layer. After curing of the resin, the circuit is formed by patterning via holes through the polymer film to the components, metallising the polymer film and patterning the metal into the desired interconnect pattern. Feature sizes are readily scaled to the complexity needed by the circuit, permitting the use of lower cost and higher yield board photopatterning processes and equipment. This paper will cover the development of this low cost technology and will describe the process. It will also describe the thermal, mechanical and electrical features of this process and show actual working prototype modules.

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Conventional individual control chart design does not address the problem of the interrelation of the processes adequately. Owing to the increasing complexity of manufacturing systems as well as the problems caused by the natural variability of the systems, trial‐and‐error methods are the most commonly used technique for the implementation of the control charts. Trial‐and‐error methods are very costly, time consuming and highly disruptive to the real system. Hence, a systematic and holistic computer‐based methodology is proposed in this paper to obtain a control chart configuration which improves productivity and quality, and reduces cost. Simulation is used as a platform to conduct the control chart system design because different scenarios can be tested off‐line so that statistical process control can be performed effectively without making costly mistakes and disturbing the real system.

Experiments are fundamental to enhancing understanding of the complex industrial processes which we deal with every day. Experimental Design (ED) is a very powerful tool that assists engineers and scientist to discover a set of variables which are most important for a process and thereby provide a great insight into the way a process or system works. It is superior to traditional scientific approach or One‐Factor‐At‐A‐Time (OFAT) approach to experimentation, still often used today. This paper illustrates some fundamental and practical issues that every industrial engineer should know about ED. These issues include factor effects, interactions, response or quality characteristics, randomisation, replication, analysis of variance and contour plots. The paper also presents an example to demonstrate the above issues. The problems and gaps in ED in the state‐of‐the‐art will also be highlighted.

This study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.

This is a case study within an Irish meat processor where the structured Define, Measure, Analyse, Improve and Control (DMAIC) methodology was utilised along with statistical analysis to highlight areas of the meat boning process to improve.

The project led to using Six Sigma to identify and measure areas of process variation. This resulted in eliminating over-trimming of meat cuts, improving process capabilities, increasing revenue and reducing meat wastage. In addition, key performance indicators and control charts, meat-cutting templates and smart cutting lasers were implemented.

The study is one of Irish meat processors' first Six Sigma applications. The wider food and meat processing industries can leverage the learnings to understand, measure and minimise variation to enhance revenue.

Organisations can use this study to understand the benefits of adopting Six Sigma, particularly in the food industry and how measuring process variation can affect quality.

This is the first practical case study on Six sigma deployment in an Irish meat processor, and the study can be used to benchmark how Six Sigma tools can aid in understanding variation, thus benefiting key performance metrics.