Query: predictive maintenance in semiconductor etch tool

Candidate papers:

abstract: Increasing yield, eliminating unexpected downtime, and reducing maintenance costs are some of the potential benefits that a predictive monitoring system can provide for semiconductor manufacturing. Prior to the implementation of a predictive based condition monitoring approach for critical semiconductor equipment and components, the technology requires further development and validation. A systematic approach for developing a predictive monitoring system is presented, including several key steps that include data pre processing, feature extraction and selection, health assessment, and component lifetime prediction. The approach is demonstrated on a LAM 2300(r) Kiyo(r) conductor etch product, in which data was collected over an 8 month period from a GlobalFoundries Fab in Dresden, Germany. The methodology and predictive health models are demonstrated for an example component from the etching tool, the electrostatic chuck. Both trace signals and metrology data are evaluated for developing the health and prediction models for the electrostatic chuck. The health monitoring models provide a clear and increasing trend, in which the electrostatic health value can be tracked quite well over time. The systematic development framework shows promise and future works looks to further validate the prediction models, and in addition develop commercially viable implementation software from this work.
author_names: Jay Lee

David Siegel

Edzel Lapira
title: Development of a Predictive and Preventive Maintenance Demonstration System for a Semiconductor Etching Tool
year: 2013

abstract: In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.
author_names: Jian Wan

Sean F McLoone

Patrick English

Paul G O'Hara

Adrian B Johnston
title: Predictive Maintenance for Improved Sustainability An Ion Beam Etch Endpoint Detection System Use Case
year: 2014

abstract: An adaptive neural network based advanced process control software, the Dynamic Neural Controller/spl trade/ (DNC) was employed at National Semiconductor's 200 mm fabrication facility, South Portland, Maine, to enhance the performance of metal etch tools. The installation was performed on 5 identical LAM 9600 TCP Metal etchers running production material. The DNC produced a single predictive model on critical outputs and metrology for each tool based on process variables, maintenance, input metrology and output metrology. Although process metrology is usually measured on only one wafer per lot, the process can be closely monitored on a wafer by wafer basis with the DNC models. The DNC was able to provide recommendations for maintenance (replacing components in advance of predicted failure) and process variable adjustments (e.g. gas flow) to maximize tool up time and to reduce scrap. This enabled the equipment engineers to both debug problems more quickly on the tool and to make adjustments to tool parameters before out of spec wafers were produced. After a comparison of the performance of all 5 tools for a 2 month period prior to DNC installation vs. a 2 month post DNC period, we concluded that the software was able to predict when maintenance actions were required, when process changes were required, and when maintenance actions were being taken but were not required. We observed a significant improvement in process C/sub pk/s for the metal etchers in this study.
author_names: John M Hyde

Julie Doxsey

Jill P Card
title: The use of unified APC/FD in the control of a metal etch area
venue: 2004 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (IEEE Cat. No.04CH37530)
year: 2004

abstract: In order to occupy a competitive position in semiconductor industry the most important challenges a fabrication plant has to face are the reduction of manufacturing costs and the increase of production yield. Predictive maintenance is one possible way to address these challenges. In this paper we present an implementation of a universally applicable methodology based on the theory of regression trees and Random Forests to predict tool maintenance operations. We exemplarily show the application of the method by constructing a model for predictive maintenance of an ion implantation tool. To fit the problem adequately and to allow a descriptive interpretation we introduce the remaining time until next maintenance as a response variable. By using R and adequately analyzing data acquired during wafer processing a Random Forest model is constructed. We can show that under typical production conditions the model is able to predict a recurring maintenance operation sufficiently accurate. This example shows that better planning of maintenance operations allows for an increase in productivity and a reduction of downtime costs.
author_names: Peter Scheibelhofer

Dietmar Gleispach

Guenther Hayderer

Ernst Stadlober
title: A Methodology for Predictive Maintenance in Semiconductor Manufacturing
year: 2016

abstract: Over the past two years the Predictive Maintenance (PdM) capability in semiconductor manufacturing has migrated from Proof of Concept (PoC) and univariate Fault Detection (FD) extrapolation mechanisms to fab wide solutions that are (1) robust to typical process and equipment disturbances, (2) extensible so as to provide solution approaches that are portable across instances of a tool type and across tool types, and (3) maintainable so as to provide solutions that are useful for long periods of time. A number of advancements have facilitated this advancement including solutions for porting modeling components across process and equipment types, mechanisms for incorporating process and equipment knowledge into models, mechanisms for determining model context (e.g. recipe) dependency, methods for model optimization to fab financials, and methods for rejecting run time disturbances in PdM modeling. As a result of these and other innovations, the landscape of PdM in semiconductor manufacturing has rapidly advanced to the point that, from a technical perspective, solutions are now available for fab wide PdM realization.
author_names: Jimmy Iskandar

James R Moyne

K Subrahmanyam

Parris C M Hawkins

Michael Armacost
title: Predictive Maintenance in semiconductor manufacturing
venue: 2015 26th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2015

abstract: Predictive maintenance is considered as one of the helpful techniques to improve the manufacturing process, especially in high mixed semiconductor manufacturing suffered from the frequent tool failing behavior. Prognostics is a good solution to infering the failing occurrence and realizing predictive maintenance. Because of the complicated and unclear tool failing phenomena, it is difficult to model the tool failing behavior by mechanism analysis. The difficulty impedes the development of prognostics in semiconductor manufacturing. As data driven methods aim at estimating future behaviors without knowledge of the underlying failing phenomena, this article proposes a novel data driven prognostic method based on auto associative Gaussian process regression to infer the tool failing behavior in semiconductor manufacturing. Through extracting the failing factors, determining the failing degree and constructing the prognostic model, the tool failing behavior tendency is predicted and the suitable chamber cleaning time is determined to improve the productivity and save the cost of semiconductor manufacturing. The effectiveness and practicality of the proposed method are demonstrated by a practical semiconductor manufacturing factory. The obtained prognostic results can help operators understand the tool failing behavior better and guide decision makers to make a suitable plan for chamber cleaning in semiconductor manufacturing.
author_names: Li Zhu

Junghui Chen

Chun-I Chen
title: Prognostics of tool failing behavior based on autoassociative Gaussian process regression for semiconductor manufacturing
venue: 2020 IEEE International Conference on Industrial Technology (ICIT)
year: 2020

abstract: In this paper, an online predictive maintenance approach is proposed for monitoring health of semiconductor equipment. It includes two phases, the first is online prediction of the health indicator and the second phase is the classification of the indicator to one of the health states for making maintenance decisions. Kernel recursive least square (KRLS) algorithm is used for online prediction which is computational efficient. The health states of the equipment can be defined based on the requirement specification for the equipment maintenance. The classification is used in the second stage based on the prediction results come from the first stage. The approach is tested with a simulated dataset from a semiconductor tool and results show a relative high accuracy can be achieved with a satisfactory computational efficiency.
author_names: Ming Luo

Zhao Xu

Hian-Leng Chan

Marjan Alavi
title: Online predictive maintenance approach for semiconductor equipment
venue: IECON 2013 39th Annual Conference of the IEEE Industrial Electronics Society
year: 2013

abstract: The unexpected failure of modern industrial systems is often managed using data driven predictive maintenance (PdM) tools that continuously monitor a system's health condition (HC) through raw sensor data to predict impending malfunction. However, research demonstrates that data driven PdM tools perform poorly when applied to raw multi sensor data, as it is not guaranteed that all sensor data describe a system's health condition. As systems become more complex and require multi sensor measurements, to perform accurate data driven PdM, an accurate health condition representation of a system's life cycle data is required. This work introduces a Kullback Leibler divergence (KLD) based health indicator (HI) constructor for multi sensor systems. This method applies information entropy to construct a HI representation that describes the occurrence of faults and their influences during a system's life cycle. Additionally, the proposed method conducts feature selection to expose and remove sensors that do not capture information related to a system's HC. The utility of the proposed method is tested on the Commercial Modular Aero Propulsion System Simulation turbofan engine data and the OEM Group's Cintillio SAT Batch Spray semiconductor manufacturing equipment data.
author_names: Oluseun Omotola Aremu

Darren O O'Reilly

David Hyland-Wood

Peter Ross McAree
title: Kullback Leibler Divergence Constructed Health Indicator for Data Driven Predictive Maintenance of Multi Sensor Systems
venue: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
year: 2019

abstract: In this paper an approach to deal with Predictive Maintenance (PdM) problems with time series data is discussed. PdM is a important approach to tackle maintenance and it is gaining an increasing attention in advanced manufacturing to minimize scrap materials, downtime, and associated costs. PdM approaches are generally based on Machine Learning tools that require the availability of historical process and maintenance data. Given the exponential growth in data logging in modern equipment, time series dataset are increasingly available in PdM applications. To exploit time series data for PdM, a functional learning methodology, namely Supervised Aggregative Feature Extraction (SAFE) is here employed on a semiconductor manufacturing maintenance problem.
author_names: Gian Antonio Susto

Alessandro Beghi
title: Dealing with time series data in Predictive Maintenance problems
venue: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)
year: 2016

abstract: Ion Implantation is one of the most sensitive processes in Semiconductor Manufacturing. It consists in impacting accelerated ions with a material substrate and is performed by an Implanter tool. The major maintenance issue of such tool concerns the breaking of the tungsten filament contained within the ion source of the tool. This kind of fault can happen on a weekly basis, and the associated maintenance operations can last up to 3 hours. It is important to optimize the maintenance activities by synchronizing the Filament change operations with other minor maintenance interventions. In this paper, a Predictive Maintenance (PdM) system is proposed to tackle such issue; the filament lifetime is estimated on a statistical basis exploiting the knowledge of physical variables acting on the process. Given the high dimensionality of the data, the statistical modeling has been based on Regularization Methods: Lasso, Ridge Regression and Elastic Nets. The predictive performances of the aforementioned regularization methods and of the proposed PdM module have been tested on actual productive semiconductor data.
author_names: Gian Antonio Susto

Andrea Schirru

Simone Pampuri

Alessandro Beghi
title: A predictive maintenance system based on regularization methods for ion implantation
venue: 2012 SEMI Advanced Semiconductor Manufacturing Conference
year: 2012