2,485 research outputs found

    A review of data mining applications in semiconductor manufacturing

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    The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

    Defect cluster recognition system for fabricated semiconductor wafers

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    The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type)

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Capacity Analysis of Automated Material Handling Systems in Semiconductor Fabs

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    A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control polices to optimize fab performance. As wafer sizes have increased, semiconductor fabs have moved to-ward the use of automated material handling systems (AMHS). However, the behavior of AMHS and the effects of AMHS on fab productivity is not well understood. This research involves the development of a design and analysis methodology for evaluating the throughput capacity of AMHS. A set of simulation experiments is used to evaluate the throughput capacity of an AMHS and the effects on fab performance measures. The analysis uses SEMATECH fab data for full semiconductor fabs to evaluate the AMHS throughput capacity

    ์ œ์กฐ ์‹œ์Šคํ…œ์—์„œ์˜ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•œ ์ง€๋Šฅ์  ๋ฐ์ดํ„ฐ ํš๋“

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ์กฐ์„ฑ์ค€.Predictive modeling is a type of supervised learning to find the functional relationship between the input variables and the output variable. Predictive modeling is used in various aspects in manufacturing systems, such as automation of visual inspection, prediction of faulty products, and result estimation of expensive inspection. To build a high-performance predictive model, it is essential to secure high quality data. However, in manufacturing systems, it is practically impossible to acquire enough data of all kinds that are needed for the predictive modeling. There are three main difficulties in the data acquisition in manufacturing systems. First, labeled data always comes with a cost. In many problems, labeling must be done by experienced engineers, which is costly. Second, due to the inspection cost, not all inspections can be performed on all products. Because of time and monetary constraints in the manufacturing system, it is impossible to obtain all the desired inspection results. Third, changes in the manufacturing environment make data acquisition difficult. A change in the manufacturing environment causes a change in the distribution of generated data, making it impossible to obtain enough consistent data. Then, the model have to be trained with a small amount of data. In this dissertation, we overcome this difficulties in data acquisition through active learning, active feature-value acquisition, and domain adaptation. First, we propose an active learning framework to solve the high labeling cost of the wafer map pattern classification. This makes it possible to achieve higher performance with a lower labeling cost. Moreover, the cost efficiency is further improved by incorporating the cluster-level annotation into active learning. For the inspection cost for fault prediction problem, we propose a active inspection framework. By selecting products to undergo high-cost inspection with the novel uncertainty estimation method, high performance can be obtained with low inspection cost. To solve the recipe transition problem that frequently occurs in faulty wafer prediction in semiconductor manufacturing, a domain adaptation methods are used. Through sequential application of unsupervised domain adaptation and semi-supervised domain adaptation, performance degradation due to recipe transition is minimized. Through experiments on real-world data, it was demonstrated that the proposed methodologies can overcome the data acquisition problems in the manufacturing systems and improve the performance of the predictive models.์˜ˆ์ธก ๋ชจ๋ธ๋ง์€ ์ง€๋„ ํ•™์Šต์˜ ์ผ์ข…์œผ๋กœ, ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ์ž…๋ ฅ ๋ณ€์ˆ˜์™€ ์ถœ๋ ฅ ๋ณ€์ˆ˜ ๊ฐ„์˜ ํ•จ์ˆ˜์  ๊ด€๊ณ„๋ฅผ ์ฐพ๋Š” ๊ณผ์ •์ด๋‹ค. ์ด๋Ÿฐ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์€ ์œก์•ˆ ๊ฒ€์‚ฌ ์ž๋™ํ™”, ๋ถˆ๋Ÿ‰ ์ œํ’ˆ ์‚ฌ์ „ ํƒ์ง€, ๊ณ ๋น„์šฉ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ ์ถ”์ • ๋“ฑ ์ œ์กฐ ์‹œ์Šคํ…œ ์ „๋ฐ˜์— ๊ฑธ์ณ ํ™œ์šฉ๋œ๋‹ค. ๋†’์€ ์„ฑ๋Šฅ์˜ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์–‘์งˆ์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ํ•˜์ง€๋งŒ ์ œ์กฐ ์‹œ์Šคํ…œ์—์„œ ์›ํ•˜๋Š” ์ข…๋ฅ˜์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์›ํ•˜๋Š” ๋งŒํผ ํš๋“ํ•˜๋Š” ๊ฒƒ์€ ํ˜„์‹ค์ ์œผ๋กœ ๊ฑฐ์˜ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ฐ์ดํ„ฐ ํš๋“์˜ ์–ด๋ ค์›€์€ ํฌ๊ฒŒ ์„ธ๊ฐ€์ง€ ์›์ธ์— ์˜ํ•ด ๋ฐœ์ƒํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ๋กœ, ๋ผ๋ฒจ๋ง์ด ๋œ ๋ฐ์ดํ„ฐ๋Š” ํ•ญ์ƒ ๋น„์šฉ์„ ์ˆ˜๋ฐ˜ํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ๋งŽ์€ ๋ฌธ์ œ์—์„œ, ๋ผ๋ฒจ๋ง์€ ์ˆ™๋ จ๋œ ์—”์ง€๋‹ˆ์–ด์— ์˜ํ•ด ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•˜๊ณ , ์ด๋Š” ํฐ ๋น„์šฉ์„ ๋ฐœ์ƒ์‹œํ‚จ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, ๊ฒ€์‚ฌ ๋น„์šฉ ๋•Œ๋ฌธ์— ๋ชจ๋“  ๊ฒ€์‚ฌ๊ฐ€ ๋ชจ๋“  ์ œํ’ˆ์— ๋Œ€ํ•ด ์ˆ˜ํ–‰๋  ์ˆ˜ ์—†๋‹ค. ์ œ์กฐ ์‹œ์Šคํ…œ์—๋Š” ์‹œ๊ฐ„์ , ๊ธˆ์ „์  ์ œ์•ฝ์ด ์กด์žฌํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์›ํ•˜๋Š” ๋ชจ๋“  ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ๊ฐ’์„ ํš๋“ํ•˜๋Š” ๊ฒƒ์ด ์–ด๋ ต๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ, ์ œ์กฐ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๊ฐ€ ๋ฐ์ดํ„ฐ ํš๋“์„ ์–ด๋ ต๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ œ์กฐ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๋Š” ์ƒ์„ฑ๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ๋ถ„ํฌ๋ฅผ ๋ณ€ํ˜•์‹œ์ผœ, ์ผ๊ด€์„ฑ ์žˆ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์ถฉ๋ถ„ํžˆ ํš๋“ํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ํ•œ๋‹ค. ์ด๋กœ ์ธํ•ด ์ ์€ ์–‘์˜ ๋ฐ์ดํ„ฐ๋งŒ์œผ๋กœ ๋ชจ๋ธ์„ ์žฌํ•™์Šต์‹œ์ผœ์•ผ ํ•˜๋Š” ์ƒํ™ฉ์ด ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋ฐœ์ƒํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฐ ๋ฐ์ดํ„ฐ ํš๋“์˜ ์–ด๋ ค์›€์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋Šฅ๋™ ํ•™์Šต, ๋Šฅ๋™ ํ”ผ์ณ๊ฐ’ ํš๋“, ๋„๋ฉ”์ธ ์ ์‘ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•œ๋‹ค. ๋จผ์ €, ์›จ์ดํผ ๋งต ํŒจํ„ด ๋ถ„๋ฅ˜ ๋ฌธ์ œ์˜ ๋†’์€ ๋ผ๋ฒจ๋ง ๋น„์šฉ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋Šฅ๋™ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ ์€ ๋ผ๋ฒจ๋ง ๋น„์šฉ์œผ๋กœ ๋†’์€ ์„ฑ๋Šฅ์˜ ๋ถ„๋ฅ˜ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‚˜์•„๊ฐ€, ๊ตฐ์ง‘ ๋‹จ์œ„์˜ ๋ผ๋ฒจ๋ง ๋ฐฉ๋ฒ•์„ ๋Šฅ๋™ํ•™์Šต์— ์ ‘๋ชฉํ•˜์—ฌ ๋น„์šฉ ํšจ์œจ์„ฑ์„ ํ•œ์ฐจ๋ก€ ๋” ๊ฐœ์„ ํ•œ๋‹ค. ์ œํ’ˆ ๋ถˆ๋Ÿ‰ ์˜ˆ์ธก์— ํ™œ์šฉ๋˜๋Š” ๊ฒ€์‚ฌ ๋น„์šฉ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋Šฅ๋™ ๊ฒ€์‚ฌ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ถˆํ™•์‹ค์„ฑ ์ถ”์ • ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ณ ๋น„์šฉ ๊ฒ€์‚ฌ ๋Œ€์ƒ ์ œํ’ˆ์„ ์„ ํƒํ•จ์œผ๋กœ์จ ์ ์€ ๊ฒ€์‚ฌ ๋น„์šฉ์œผ๋กœ ๋†’์€ ์„ฑ๋Šฅ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๋ฐ˜๋„์ฒด ์ œ์กฐ์˜ ์›จ์ดํผ ๋ถˆ๋Ÿ‰ ์˜ˆ์ธก์—์„œ ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๋Š” ๋ ˆ์‹œํ”ผ ๋ณ€๊ฒฝ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋„๋ฉ”์ธ ์ ์‘ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•œ๋‹ค. ๋น„๊ต์‚ฌ ๋„๋ฉ”์ธ ์ ์‘๊ณผ ๋ฐ˜๊ต์‚ฌ ๋„๋ฉ”์ธ ์ ์‘์˜ ์ˆœ์ฐจ์ ์ธ ์ ์šฉ์„ ํ†ตํ•ด ๋ ˆ์‹œํ”ผ ๋ณ€๊ฒฝ์— ์˜ํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ตœ์†Œํ™”ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ค์ œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋ก ๋“ค์ด ์ œ์กฐ์‹œ์Šคํ…œ์˜ ๋ฐ์ดํ„ฐ ํš๋“ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ์˜ˆ์ธก ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ๋†’์ผ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.1. Introduction 1 2. Literature Review 9 2.1 Review of Related Methodologies 9 2.1.1 Active Learning 9 2.1.2 Active Feature-value Acquisition 11 2.1.3 Domain Adaptation 14 2.2 Review of Predictive Modelings in Manufacturing 15 2.2.1 Wafer Map Pattern Classification 15 2.2.2 Fault Detection and Classification 16 3. Active Learning for Wafer Map Pattern Classification 19 3.1 Problem Description 19 3.2 Proposed Method 21 3.2.1 System overview 21 3.2.2 Prediction model 25 3.2.3 Uncertainty estimation 25 3.2.4 Query wafer selection 29 3.2.5 Query wafer labeling 30 3.2.6 Model update 30 3.3 Experiments 31 3.3.1 Data description 31 3.3.2 Experimental design 31 3.3.3 Results and discussion 34 4. Active Cluster Annotation for Wafer Map Pattern Classification 42 4.1 Problem Description 42 4.2 Proposed Method 44 4.2.1 Clustering of unlabeled data 46 4.2.2 CNN training with labeled data 48 4.2.3 Cluster-level uncertainty estimation 49 4.2.4 Query cluster selection 50 4.2.5 Cluster-level annotation 50 4.3 Experiments 51 4.3.1 Data description 51 4.3.2 Experimental setting 51 4.3.3 Clustering results 53 4.3.4 Classification performance 54 4.3.5 Analysis for label noise 57 5. Active Inspection for Fault Prediction 60 5.1 Problem Description 60 5.2 Proposed Method 65 5.2.1 Active inspection framework 65 5.2.2 Acquisition based on Expected Prediction Change 68 5.3 Experiments 71 5.3.1 Data description 71 5.3.2 Fault prediction models 72 5.3.3 Experimental design 73 5.3.4 Results and discussion 74 6. Adaptive Fault Detection for Recipe Transition 76 6.1 Problem Description 76 6.2 Proposed Method 78 6.2.1 Overview 78 6.2.2 Unsupervised adaptation phase 81 6.2.3 Semi-supervised adaptation phase 83 6.3 Experiments 85 6.3.1 Data description 85 6.3.2 Experimental setting 85 6.3.3 Performance degradation caused by recipe transition 86 6.3.4 Effect of unsupervised adaptation 87 6.3.5 Effect of semi-supervised adaptation 88 7. Conclusion 91 7.1 Contributions 91 7.2 Future work 94Docto

    Discovering correlated parameters in Semiconductor Manufacturing processes: a Data Mining approach

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    International audienceData mining tools are nowadays becoming more and more popular in the semiconductor manufacturing industry, and especially in yield-oriented enhancement techniques. This is because conventional approaches fail to extract hidden relationships between numerous complex process control parameters. In order to highlight correlations between such parameters, we propose in this paper a complete knowledge discovery in databases (KDD) model. The mining heart of the model uses a new method derived from association rules programming, and is based on two concepts: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of values of a relation and the values of sets of targets belonging to the same relation. Decision correlation rules are built on the twofold basis of the chi-squared measure and of the support of the extracted values. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables. This algorithm is the basis of our KDD model software, called MineCor. An overall presentation of its other functions, of some significant experimental results, and of associated performances are provided and discussed

    Towards an on-chip power supply: Integration of micro energy harvesting and storage techniques for wireless sensor networks

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    The lifetime of a power supply in a sensor node of a wireless sensor network is the decisive factor in the longevity of the system. Traditional Li-ion batteries cannot fulfill the demands of sensor networks that require a long operational duration. Thus, we require a solution that produces its own electricity from its surrounding and stores it for future utility. Moreover, as the sensor node architecture is developed on complimentary metal-oxide-semiconductor technology (CMOS), the manufacture of the power supply must be compatible with it. In this thesis, we shall describe the components of an on-chip lifetime power supply that can harvest the vibrational mechanical energy through piezoelectric microcantilevers and store it in a reduced graphene oxide (rGO) based microsupercapacitor, and that is fabricated through CMOS compatible techniques. Our piezoelectric microcantilevers confirm the feasibility of fabricating micro electro- mechanical-systems (MEMS) size two-degree-of-freedom systems which can solve the major issue of small bandwidth of piezoelectric micro-energy harvesters. These devices use a cut-out trapezoidal cantilever beam to enhance the stress on the cantileverโ€™s free end while reducing the gap remarkably between its first two eigenfrequencies in 400 - 500 Hz and 1 - 2 kHz range. The energy from the M-shaped harvesters will be stored in rGO based microsupercapacitors. These microsupercapacitors are manufactured through a fully CMOS compatible, reproducible, and reliable micromachining processes. Furthermore, we have also demonstrated an improvement in their electrochemical performance and yield of fabrication through surface roughening from iron nanoparticles. We have also examined the possibility of integrating these devices into a power management unit to fully realize a lifetime power supply for wireless sensor networks

    Data mining in manufacturing: a review based on the kind of knowledge

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    In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

    Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement

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    The purpose of this chapter is to outline systematic implementation of the Six Sigma DMAIC methodology as a case study in solving the problem of poor wafer yields in semiconductor manufacturing. The chapter also describes well-known industry standard business processes to be implemented and benchmarked in a semiconductor wafer fabrication facility to manage defect and yield issues while executing a Six Sigma project. The execution of Six Sigma enabled identification of the key process factors, root cause analysis, desired performance levels, and Cpk improvement opportunities. Implementing multilevel factorial design of experiments (DOE) study revealed critical input parameters on process tools contributing to defect formation. Improvement performed on these process tools resulted in in-line defect reduction and ultimately improving final yields
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