A review of data mining applications in semiconductor manufacturing

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

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

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

Defect cluster recognition system for fabricated semiconductor wafers

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)

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

제조 시스템에서의 예측 모델링을 위한 지능적 데이터 획득

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 조성준.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.예측 모델링은 지도 학습의 일종으로, 학습 데이터를 통해 입력 변수와 출력 변수 간의 함수적 관계를 찾는 과정이다. 이런 예측 모델링은 육안 검사 자동화, 불량 제품 사전 탐지, 고비용 검사 결과 추정 등 제조 시스템 전반에 걸쳐 활용된다. 높은 성능의 예측 모델을 달성하기 위해서는 양질의 데이터가 필수적이다. 하지만 제조 시스템에서 원하는 종류의 데이터를 원하는 만큼 획득하는 것은 현실적으로 거의 불가능하다. 데이터 획득의 어려움은 크게 세가지 원인에 의해 발생한다. 첫번째로, 라벨링이 된 데이터는 항상 비용을 수반한다는 점이다. 많은 문제에서, 라벨링은 숙련된 엔지니어에 의해 수행되어야 하고, 이는 큰 비용을 발생시킨다. 두번째로, 검사 비용 때문에 모든 검사가 모든 제품에 대해 수행될 수 없다. 제조 시스템에는 시간적, 금전적 제약이 존재하기 때문에, 원하는 모든 검사 결과값을 획득하는 것이 어렵다. 세번째로, 제조 환경의 변화가 데이터 획득을 어렵게 만든다. 제조 환경의 변화는 생성되는 데이터의 분포를 변형시켜, 일관성 있는 데이터를 충분히 획득하지 못하게 한다. 이로 인해 적은 양의 데이터만으로 모델을 재학습시켜야 하는 상황이 빈번하게 발생한다. 본 논문에서는 이런 데이터 획득의 어려움을 극복하기 위해 능동 학습, 능동 피쳐값 획득, 도메인 적응 방법을 활용한다. 먼저, 웨이퍼 맵 패턴 분류 문제의 높은 라벨링 비용을 해결하기 위해 능동학습 프레임워크를 제안한다. 이를 통해 적은 라벨링 비용으로 높은 성능의 분류 모델을 구축할 수 있다. 나아가, 군집 단위의 라벨링 방법을 능동학습에 접목하여 비용 효율성을 한차례 더 개선한다. 제품 불량 예측에 활용되는 검사 비용 문제를 해결하기 위해서는 능동 검사 방법을 제안한다. 제안하는 새로운 불확실성 추정 방법을 통해 고비용 검사 대상 제품을 선택함으로써 적은 검사 비용으로 높은 성능을 얻을 수 있다. 반도체 제조의 웨이퍼 불량 예측에서 빈번하게 발생하는 레시피 변경 문제를 해결하기 위해서는 도메인 적응 방법을 활용한다. 비교사 도메인 적응과 반교사 도메인 적응의 순차적인 적용을 통해 레시피 변경에 의한 성능 저하를 최소화한다. 본 논문에서는 실제 데이터에 대한 실험을 통해 제안된 방법론들이 제조시스템의 데이터 획득 문제를 극복하고 예측 모델의 성능을 높일 수 있음을 확인하였다.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

Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised Convolutional AutoEncoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional Convolutional Autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter set-up in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams