Search query: A virtual metrology system for semiconductor manufacturing

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abstract: Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer's quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM) on the other hand, enables to predict every wafer's metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.
author_names: Pilsung Kang

Hyoungjoo Lee

Sungzoon Cho

D Kim

Jinwoo Park

Chan-Kyoo Park

Seungyong Doh
title: A virtual metrology system for semiconductor manufacturing
venue: Expert Syst. Appl.
year: 2009

abstract: Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unne.
title: A virtual metrology system for semiconductor manufacturing
year: 2009

abstract: Abstract Virtual metrology involves the estimation of metrology values using a prediction model instead of metrological equipment, thereby providing an efficient means for wafer to wafer quality control. Because wafer characteristics change over time according to the influence of several factors in the manufacturing process, the prediction model should be suitably updated in view of recent actual metrology results. This gives rise to a trade off relationship, as more frequent updates result in a higher accuracy for virtual metrology, while also incurring a heavier cost in actual metrology. In this paper, we propose an intelligent virtual metrology system to achieve a superior metrology performance with lower costs. By employing an ensemble of artificial neural networks as the prediction model, the prediction, reliability estimation, and model update are successfully integrated into the proposed virtual metrology system. In this system, actual metrology is only performed for those wafers where the current prediction model cannot perform reliable predictions. When actual metrology is performed, the prediction model is instantly updated to incorporate the results. Consequently, the actual metrology ratio is automatically adjusted according to the corresponding circumstances. We demonstrate the effectiveness of the method through experimental validation on actual datasets.
author_names: Seokho Kang

Pilsung Kang
title: An intelligent virtual metrology system with adaptive update for semiconductor manufacturing
year: 2017

abstract: Automatic virtual metrology (AVM) is the highest level technology for virtual metrology (VM) applications from the perspective of automation, which could facilitate fast factory wide deployment of VM systems. However, the existing AVM system suffered several limitations during its practical deployment and operation in a fab for semiconductor manufacturing. In this paper, by leveraging the advantages of cloud computing, we propose an approach of building cloud based AVM systems, which can effectively resolve these limitations. First, a cloud based architecture is designed based on a private cloud to virtualize all servers of the AVM system for resolving the limitations of using physical servers, such as incurring high hardware cost, occupying a lot of shop floor space, and needing complex efforts in managing VM servers. Then, three automatic functional mechanisms (i.e. automatic deployment mechanism, automatic scaling mechanism, and automatic serving mechanism) are developed in an extra server (i.e. the virtual machine administrator server) to automate the deployment of VM servers, to automatically scale out/in the number of VM servers on demand, and to automatically dispatch VM servers to serve the requested VM tasks in parallel. Such an architecture design could significantly reduce the efforts of migrating the original AVM system to the cloud. Integrated testing results show that the proposed cloud based AVM system could successfully overcome the limitations of the existing AVM system, while demonstrating a significant performance improvement over the existing AVM system in predicting the production quality of wafers. Most existing VM related literature focused on the development of the VM models. To our knowledge, no papers have coped with the issues of plant wide deployment and operation of VM systems by using cloud computing. This paper could be a useful reference for industrial practitioners to construct cloud based AVM systems. An approach of building cloud based AVM systems is proposed.A cloud based AVM architecture is designed based on private cloud.The AVM system can be transformed into a cloud based version with small efforts.The limitations of the existing AVM system are addressed and overcome.This paper could be a useful reference for constructing cloud based AVM systems.
author_names: Hsien-Cheng Huang

Yu-Chuan Lin

Min-Hsiung Hung

Chia-Chun Tu

Fan-Tien Cheng
title: Development of cloud based automatic virtual metrology system for semiconductor industry
year: 2015

abstract: In semiconductor manufacturing, optimization of the sampling measurement plan through production steps is key to maximize productive performances. The measurement plan must guarantee high quality and compliance to wafer specifications limits. In this article, the relationships between virtual metrology (VM) and actual measurements are investigated with respect to a sampling decision system (SDS) specifically, a multilevel VM strategy is relied on to provide predictive information. Such virtual measurements serve as input for the sampling decision system, which in turn suggests the optimal measurement strategy. Two approaches relying on decision theoretical concepts are discussed: the expected value of measurement information (EVofMI) and a two stage sampling decision model. The basic assumption of the SDS VM system is that it is not necessary to perform a real measurement until it is strictly needed. The two methodologies are then validated relying on simulation studies and actual chemical vapor deposition (CVD) process and measurement data. The ability of the proposed system to sample dynamically the wafer measurements in dependence of the calculated risk is then evaluated and discussed.
author_names: Daniel Kurz

Jurgen Pilz

Andrea Schirru

Simone Pampuri

Cristina De Luca
title: A sampling decision system for semiconductor manufacturing relying on virtual metrology and actual measurements
venue: Proceedings of the Winter Simulation Conference 2014
year: 2014

abstract: In semiconductor manufacturing, metrology operations are expensive and time consuming, for this reason only a certain sample of wafers is measured. With the need of highly reliable processes, the semiconductor industry aims at developing methodologies covering the gap of missing metrology information. Virtual Metrology turns out to be a promising method; it aims at predicting wafer and/or site fine metrology results in real time and free of costs. In this paper, we present a sampling decision system that relies on virtual measurements suggesting an efficient strategy for measuring productive wafers. Several methods for evaluating when a real measurement is needed (including the expected utility of measurement information, a two stage sampling decision model and wafer quality risk values) are proposed. We further provide ideas on how to assess and update the reliability of the virtual measurements in a sampling decision system (whenever real measurements become available) In this context, we introduce equipment health factors and virtual trust factors for improving the reliability of the sampling decision system. Finally, the performance of the sampling decision system is demonstrated on a set of virtual and real metrology data from the semiconductor industry. It is shown that wafer measurements are efficiently performed when really needed.
author_names: Daniel Kurz

Cristina De Luca

Jurgen Pilz
title: A Sampling Decision System for Virtual Metrology in Semiconductor Manufacturing
venue: IEEE Transactions on Automation Science and Engineering
year: 2015

abstract: In semiconductor manufacturing, metrology operations are so expensive and time consuming that only a certain number of wafers are measured. For that reason, one is interested in developing Virtual Metrology (VM) methodologies predicting wafer fine metrology results in real time and free of costs. However, currently used sampling designs do not take account of such information. In this paper, we present a sampling decision system (SDS) that relies on virtual metrology data suggesting an optimal strategy for measuring productive wafers. Considering control charts within a decision theoretical framework, the expected value of measurement information is computed by means of Monte Carlo (MC) integration; this is a way to assess the informational gain resulting from a measurement. Optimal sampling decisions are obtained using a two stage decision model. Extensions of the SDS consider bad wafer quality risk and fixed real metrology operations by cumulating past decision risks. A Bayesian conjugate Wishart model allows to update uncertainty of virtual measurements whenever a real measurement is available. The sampling decision system is extended to a set of virtual and real metrology data from the semiconductor industry. Wafer measurements are only performed when really needed.
author_names: Daniel Kurz

Cristina De Luca

Jurgen Pilz
title: Sampling Decision System in semiconductor manufacturing using Virtual Metrology
venue: 2012 IEEE International Conference on Automation Science and Engineering (CASE)
year: 2012

abstract: AbstractProcess condition was changed by preventative maintenance that may lead to the inconsistency of process output. In practice, process engineers have difficulties to discover the inconsistency that the defect wafer may have been produced before the next measurement. This study proposes a virtual metrology (VM) approach for maintenance compensation in semiconductor manufacturing. The tool process parameter streams were collected to predict the product metrology value, and a relation model was built to adjust the equipment settings for product recipe compensation. An empirical study in maintenance compensation of chemical vapor deposition (CVD) was conducted in a Taiwan semiconductor company for validation. The thickness difference caused by maintenance was reduced from 571A to 77A in training data and 564A to 210A in testing data. The results showed practical viability of the proposed approach and an intelligent system embedded with the developed algorithm has been implemented.
author_names: Kuo-Yi Lin

Chia-Yu Hsu

Hui-Chun Yu
title: A virtual metrology approach for maintenance compensation to improve yield in semiconductor manufacturing
venue: Int. J. Comput. Intell. Syst.
year: 2014

abstract: The ability to predict end of line electrical properties of wafer in semiconductor manufacturing processes is critical to developing and maintaining a high yield. However, this is difficult because an advanced wafer manufacturing process consists of 300 400 steps, and in line metrology data is only available for a few steps and for infrequently sampled wafers. Although a large amount of equipment sensor outputs are readily available for most wafers, most of the sensor variables may not be related to the end of line properties. Further, differences in end of line properties of wafers processed by tools of the same stage do not imply differences in the values of sensor variables between these tools. Thus, it is important to develop a reliable screening and model building procedure to construct a robust virtual metrology model with good generalization capability. Despite its simplicity, this approach is found to have significantly better generalization capability than nonlinear models, as well as substantial improvement in modeling and prediction capabilities of linear models that use only in line metrology. The proposed method is also evaluated by an industrial application in a local fabrication unit.
author_names: Tian-Hong Pan

Bi-Qi Sheng

David Shan-Hill Wong

Shi-Shang Jang
title: A Virtual Metrology System for Predicting End of Line Electrical Properties Using a MANCOVA Model With Tools Clustering
venue: IEEE Transactions on Industrial Informatics
year: 2011

abstract: In semiconductor manufacturing plants, monitoring of all wafers is fundamental in order to maintain good yield and high quality standards. However, this is a costly approach and in practice only few wafers in a lot are actually monitored. With a Virtual Metrology (VM) system it is possible to partly overcome the lack of physical metrology. In a VM scheme, tool data are used to predict, for every wafer, metrology measurements. In this paper, we present a VM system for a Chemical Vapor Deposition (CVD) process. Various data mining techniques are proposed. Due to the huge fragmentation of data derived from CVD's mixed production, several kind of data clustering have been adopted. The proposed models have been tested on real productive industrial data sets.
author_names: Gian Antonio Susto

Alessandro Beghi

Cristina De Luca
title: A Virtual Metrology system for predicting CVD thickness with equipment variables and qualitative clustering
venue: ETFA2011
year: 2011