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

Doctor of Philosophy

dissertationIn order to ensure high production yield of semiconductor devices, it is desirable to characterize intermediate progress towards the final product by using metrology tools to acquire relevant measurements after each sequential processing step. The metrology data are commonly used in feedback and feed-forward loops of Run-to-Run (R2R) controllers to improve process capability and optimize recipes from lot-to-lot or batch-to-batch. In this dissertation, we focus on two related issues. First, we propose a novel non-threaded R2R controller that utilizes all available metrology measurements, even when the data were acquired during prior runs that differed in their contexts from the current fabrication thread. The developed controller is the first known implementation of a non-threaded R2R control strategy that was successfully deployed in the high-volume production semiconductor fab. Its introduction improved the process capability by 8% compared with the traditional threaded R2R control and significantly reduced out of control (OOC) events at one of the most critical steps in NAND memory manufacturing. The second contribution demonstrates the value of developing virtual metrology (VM) estimators using the insight gained from multiphysics models. Unlike the traditional statistical regression techniques, which lead to linear models that depend on a linear combination of the available measurements, we develop VM models, the structure of which and the functional interdependence between their input and output variables are determined from the insight provided by the multiphysics describing the operation of the processing step for which the VM system is being developed. We demonstrate this approach for three different processes, and describe the superior performance of the developed VM systems after their first-of-a-kind deployment in a high-volume semiconductor manufacturing environment

Virtual metrology for semiconductor manufacturing applications

Per essere competitive nel mercato, le industrie di semiconduttori devono poter raggiungere elevati standard di produzione a un prezzo ragionevole. Per motivi legati tanto ai costi quanto ai tempi di esecuzione, una strategia di controllo della qualità che preveda la misurazione completa del prodotto non è attuabile; i test sono eettuati su un ristretto campione dei dati originali. Il traguardo del presente lavoro di Tesi è lo studio e l'implementazione, attraverso metodologie di modellistica tipo non lineare, di un algoritmo di metrologia virtuale (Virtual Metrology) d'ausilio al controllo di processo nella produzione di semiconduttori. Infatti, la conoscenza di una stima delle misure non realmente eseguite (misure virtuali) può rappresentare un primo passo verso la costruzione di sistemi di controllo di processo e controllo della qualità sempre più ranati ed ecienti. Da un punto di vista operativo, l'obiettivo è fornire la più accurata stima possibile delle dimensioni critiche a monte della fase di etching, a partire dai dati disponibili (includendo misurazioni da fasi di litograa e deposizione e dati di processo - ove disponibili). Le tecniche statistiche allo stato dell'arte analizzate in questo lavoro comprendono: - multilayer feedforward networks; Confronto e validazione degli algoritmi presi in esame sono stati possibili grazie ai data-set forniti da un'industria manifatturiera di semiconduttori. In conclusione, questo lavoro di Tesi rappresenta un primo passo verso la creazione di un sistema di controllo di processo e controllo della qualità evoluto e essibile, che abbia il ne ultimo di migliorare la qualità della produzione.ope

Co-Nanomet: Co-ordination of Nanometrology in Europe

Nanometrology is a subfield of metrology, concerned with the science of measurement at the nanoscale level. Today’s global economy depends on reliable measurements and tests, which are trusted and accepted internationally. It must provide the ability to measure in three dimensions with atomic resolution over large areas. For industrial application this must also be achieved at a suitable speed/throughput. Measurements in the nanometre range should be traceable back to internationally accepted units of measurement (e.g. of length, angle, quantity of matter, and force). This requires common, validated measurement methods, calibrated scientific instrumentation as well as qualified reference samples. In some areas, even a common vocabulary needs to be defined. A traceability chain for the required measurements in the nm range has been established in only a few special cases. A common strategy for European nanometrology has been defined, as captured herein, such that future nanometrology development in Europe may build out from our many current strengths. In this way, European nanotechnology will be supported to reach its full and most exciting potential. As a strategic guidance, this document contains a vision for European nanometrology 2020; future goals and research needs, building out from an evaluation of the status of science and technology in 2010. It incorporates concepts for the acceleration of European nanometrology, in support of the effective commercial exploitation of emerging nanotechnologies. The field of nanotechnology covers a breadth of disciplines, each of which has specific and varying metrological needs. To this end, a set of four core technology fields or priority themes (Engineered Nanoparticles, Nanobiotechnology, Thin Films and Structured Surfaces and Modelling & Simulation) are the focus of this review. Each represents an area within which rapid scientific development during the last decade has seen corresponding growth in or towards commercial exploitation routes. This document was compiled under the European Commission Framework Programme 7 project, Co-Nanomet. It has drawn together input from industry, research institutes, (national) metrology institutes, regulatory and standardisation bodies across Europe. Through the common work of the partners and all those interested parties who have contributed, it represents a significant collaborative European effort in this important field. In the next decade, nanotechnology can be expected to approach maturity, as a major enabling technological discipline with widespread application. This document provides a guide to the many bodies across Europe in their activities or responsibilities in the field of nanotechnology and related measurement requirements. It will support the commercial exploitation of nanotechnology, as it transitions through this next exciting decade

Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities

Automated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Architectural level delay and leakage power modelling of manufacturing process variation

PhD ThesisThe effect of manufacturing process variations has become a major issue regarding the estimation of circuit delay and power dissipation, and will gain more importance in the future as device scaling continues in order to satisfy market place demands for circuits with greater performance and functionality per unit area. Statistical modelling and analysis approaches have been widely used to reflect the effects of a variety of variational process parameters on system performance factor which will be described as probability density functions (PDFs). At present most of the investigations into statistical models has been limited to small circuits such as a logic gate. However, the massive size of present day electronic systems precludes the use of design techniques which consider a system to comprise these basic gates, as this level of design is very inefficient and error prone. This thesis proposes a methodology to bring the effects of process variation from transistor level up to architectural level in terms of circuit delay and leakage power dissipation. Using a first order canonical model and statistical analysis approach, a statistical cell library has been built which comprises not only the basic gate cell models, but also more complex functional blocks such as registers, FIFOs, counters, ALUs etc. Furthermore, other sensitive factors to the overall system performance, such as input signal slope, output load capacitance, different signal switching cases and transition types are also taken into account for each cell in the library, which makes it adaptive to an incremental circuit design. The proposed methodology enables an efficient analysis of process variation effects on system performance with significantly reduced computation time compared to the Monte Carlo simulation approach. As a demonstration vehicle for this technique, the delay and leakage power distributions of a 2-stage asynchronous micropipeline circuit has been simulated using this cell library. The experimental results show that the proposed method can predict the delay and leakage power distribution with less than 5% error and at least 50,000 times faster computation time compare to 5000-sample SPICE based Monte Carlo simulation. The methodology presented here for modelling process variability plays a significant role in Design for Manufacturability (DFM) by quantifying the direct impact of process variations on system performance. The advantages of being able to undertake this analysis at a high level of abstraction and thus early in the design cycle are two fold. First, if the predicted effects of process variation render the circuit performance to be outwith specification, design modifications can be readily incorporated to rectify the situation. Second, knowing what the acceptable limits of process variation are to maintain design performance within its specification, informed choices can be made regarding the implementation technology and manufacturer selected to fabricate the design

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included