Alternate multitask learning method for fault detection model in semiconductor manufacturing

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 조성준.반도체 제조 공정의 복잡도가 증가함에 따라 시계열 데이터를 이용한 이상 탐지 분석의 중요도가 증가하고 있다. 여러 센서를 통해 얻어진 다변량 시계열 데이터를 이용한 이상탐지 모델은 통계 모델과 전통적인 머신러닝 기법들의 한계를 극복하기 위해 합성곱 신경망과 셀프 어텐션 등의 인공신경망 기법을 중심으로 활발한 연구가 이뤄지고 있다. 인공 신경망 모델의 효과적인 학습을 위해서는 대량의 레이블 된 데이터가 필요하다. 하지만 반도체 공정의 특성상 모든 데이터에 대해 레이블을 다는 것은 고비용 작업이기 때문에 적은 데이터를 가지고도 높은 성능을 보이는 모델을 개발할 필요성이 증가하고 있다. 이번 논문에서는 레시피 1과 레시피 2에 대한 적은 양의 데이터를 이용해 서로의 이상탐지 성능을 향상시킬 수 있는 교차 다중작업학습 방법을 제안한다. 모델의 구조는 시계열 임베딩과 멀티 어텐션에서 파라미터를 공유하며 이후에 각각의 레시피에 맞는 개별 작업 레이어를 추가한 형태로 이뤄진다. 또한, 이번 논문에서는 이런 Hard 파라미터 공유 다중작업모델의 학습에서 발생하는 본질적인 문제를 개선할 수 있는 교차 학습 방법을 제안한다. 교차 다중작업학습 모델을 이용해 레시피 각각에 대해서 성능이 개선됨을 확인할 수 있었고 다양한 구조의 모델에 대해서 강건한 성능 향상을 보임을 확인하였다.As the complexity of semiconductor manufacturing process increases, the importance of fault detection analysis using time series data increases. The fault detection model using multivariate time series data obtained through various sensors has been actively studied using artificial neural network techniques such as convolutional neural network and multi-head attention to overcome the limitations of statistical model and traditional machine learning techniques. For artificial neural network models to effectively learn, a large amount of labeled data is required. However, due to the nature of the semiconductor process, labeling all data is a high cost work, so it is necessary to develop a model that shows high performance with less data. In this paper, we propose a multitask learning method that can improve each other's fault detection performance by using a small amount of data for recipe 1 and recipe 2. The structure of model shares parameters in time series embedding layer and self-attention layer, followed by the addition of individual task layers for each recipe. Additionally, we propose an alternate learning method that can solve the fundamental problem that from training the hard parameter sharing multitask model. The performance of each recipe was improved by using the alternate multitask learning model, and it was confirmed that it showed robust performance improvement for models of various structures.제 1 장 서론 1 1.1 문제 정의 1 1.2 연구 동기 및 공헌 3 1.3 논문구성 4 제 2 장 선행연구 5 2.1 시계열 분석에서 셀프 어텐션 5 2.2 다중작업학습 9 2.2.1 Hard 파라미터 공유 다중작업학습 10 2.2.2 Soft 파라미터 공유 다중작업학습 11   제 3 장 해법 13 3.1 멀티 어텐션 기반 단일 모델 (베이스라인 모델) 13 3.1.1 시계열 임베딩 레이어 14 3.1.2 멀티 어텐션 레이어 15 3.1.3 완전 연결 신경망 17 3.2 Hard 파리미터 공유 교차 다중작업학습 모델 17 3.3 교차 다중작업학습 방법 19 제 4 장 실험 결과 24 4.1 실험 설계 24 4.2 실험 결과 26 4.2.1 전체 성능 향상 비교 - F1, AUC 26 4.2.2 모델의 강건성 27 제 5 장 결론 32 5.1 결론 32 5.2 향후 연구 33 참고문헌 34 Abstract 38Maste

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

Statistical Methods for Semiconductor Manufacturing

In this thesis techniques for non-parametric modeling, machine learning, filtering and prediction and run-to-run control for semiconductor manufacturing are described. In particular, algorithms have been developed for two major applications area: - Virtual Metrology (VM) systems; - Predictive Maintenance (PdM) systems. Both technologies have proliferated in the past recent years in the semiconductor industries, called fabs, in order to increment productivity and decrease costs. VM systems aim of predicting quantities on the wafer, the main and basic product of the semiconductor industry, that may be physically measurable or not. These quantities are usually ’costly’ to be measured in economic or temporal terms: the prediction is based on process variables and/or logistic information on the production that, instead, are always available and that can be used for modeling without further costs. PdM systems, on the other hand, aim at predicting when a maintenance action has to be performed. This approach to maintenance management, based like VM on statistical methods and on the availability of process/logistic data, is in contrast with other classical approaches: - Run-to-Failure (R2F), where there are no interventions performed on the machine/process until a new breaking or specification violation happens in the production; - Preventive Maintenance (PvM), where the maintenances are scheduled in advance based on temporal intervals or on production iterations. Both aforementioned approaches are not optimal, because they do not assure that breakings and wasting of wafers will not happen and, in the case of PvM, they may lead to unnecessary maintenances without completely exploiting the lifetime of the machine or of the process. The main goal of this thesis is to prove through several applications and feasibility studies that the use of statistical modeling algorithms and control systems can improve the efficiency, yield and profits of a manufacturing environment like the semiconductor one, where lots of data are recorded and can be employed to build mathematical models. We present several original contributions, both in the form of applications and methods. The introduction of this thesis will be an overview on the semiconductor fabrication process: the most common practices on Advanced Process Control (APC) systems and the major issues for engineers and statisticians working in this area will be presented. Furthermore we will illustrate the methods and mathematical models used in the applications. We will then discuss in details the following applications: - A VM system for the estimation of the thickness deposited on the wafer by the Chemical Vapor Deposition (CVD) process, that exploits Fault Detection and Classification (FDC) data is presented. In this tool a new clustering algorithm based on Information Theory (IT) elements have been proposed. In addition, the Least Angle Regression (LARS) algorithm has been applied for the first time to VM problems. - A new VM module for multi-step (CVD, Etching and Litography) line is proposed, where Multi-Task Learning techniques have been employed. - A new Machine Learning algorithm based on Kernel Methods for the estimation of scalar outputs from time series inputs is illustrated. - Run-to-Run control algorithms that employ both the presence of physical measures and statistical ones (coming from a VM system) is shown; this tool is based on IT elements. - A PdM module based on filtering and prediction techniques (Kalman Filter, Monte Carlo methods) is developed for the prediction of maintenance interventions in the Epitaxy process. - A PdM system based on Elastic Nets for the maintenance predictions in Ion Implantation tool is described. Several of the aforementioned works have been developed in collaborations with major European semiconductor companies in the framework of the European project UE FP7 IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance); such collaborations will be specified during the thesis, underlying the practical aspects of the implementation of the proposed technologies in a real industrial environment

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

Approaches of production planning and control under Industry 4.0: A literature review

Purpose: Industry 4.0 technologies significantly impact how production is planned, scheduled, and controlled. Literature provides different classifications of the tasks and functions of production planning and control (PPC) like the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model. It extends a reduced version that has been published previously in Procedia Computer Science (Herrmann, Tackenberg, Padoano & Gamber, 2021) by presenting and discussing its results in more detail. Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model’s tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches mainly focus on production control using real-time information from the shop floor, part of in-house PPC. We discuss the different layers of the cyber-physical PPC architecture and propose future research directions for the unexplored tasks and functions of the Aachen PPC model. Research limitations/implications: Limitations are the strong dependence of results on search terms used and the subjective eligibility assessment and assignment of publications to the Aachen PPC model. The selection of search terms and the texts’ interpretation is based on an individual’s assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently. Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0Peer Reviewe

Computational Intelligence Techniques for OES Data Analysis

Semiconductor manufacturers are forced by market demand to continually deliver lower cost and faster devices. This results in complex industrial processes that, with continuous evolution, aim to improve quality and reduce costs. Plasma etching processes have been identified as a critical part of the production of semiconductor devices. It is therefore important to have good control over plasma etching but this is a challenging task due to the complex physics involved. Optical Emission Spectroscopy (OES) measurements can be collected non-intrusively during wafer processing and are being used more and more in semiconductor manufacturing as they provide real time plasma chemical information. However, the use of OES measurements is challenging due to its complexity, high dimension and the presence of many redundant variables. The development of advanced analysis algorithms for virtual metrology, anomaly detection and variables selection is fundamental in order to effectively use OES measurements in a production process. This thesis focuses on computational intelligence techniques for OES data analysis in semiconductor manufacturing presenting both theoretical results and industrial application studies. To begin with, a spectrum alignment algorithm is developed to align OES measurements from different sensors. Then supervised variables selection algorithms are developed. These are defined as improved versions of the LASSO estimator with the view to selecting a more stable set of variables and better prediction performance in virtual metrology applications. After this, the focus of the thesis moves to the unsupervised variables selection problem. The Forward Selection Component Analysis (FSCA) algorithm is improved with the introduction of computationally efficient implementations and different refinement procedures. Nonlinear extensions of FSCA are also proposed. Finally, the fundamental topic of anomaly detection is investigated and an unsupervised variables selection algorithm tailored to anomaly detection is developed. In addition, it is shown how OES data can be effectively used for semi-supervised anomaly detection in a semiconductor manufacturing process. The developed algorithms open up opportunities for the effective use of OES data for advanced process control. All the developed methodologies require minimal user intervention and provide easy to interpret models. This makes them practical for engineers to use during production for process monitoring and for in-line detection and diagnosis of process issues, thereby resulting in an overall improvement in production performance

AI/ML Algorithms and Applications in VLSI Design and Technology

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

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes

Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials