Query: Predictive maintenance in semiconductor

Candidate papers:

abstract: Predictive Maintenance (PdM) offers one possibility to improve productivity in semiconductor manufacturing. Current research on PdM mainly focuses on its technical implementation. By applying discrete event simulation, we provide results how maintenance strategies influence operational performance, and how PdM contributes to an overall improvement of productivity in wafer fabrication.
author_names: K -H G Bae

B Feng

Sin-Bom Kim

Sanja Lazarova-Molnar

Z Zheng

T Roeder

Renee M Thiesing
title: USING DISCRETE EVENT SIMULATION FOR POTENTIAL ANALYSIS OF PREDICTIVE MAINTENANCE IN SEMICONDUCTOR MANUFACTURING
year: 2020

abstract: The accuracy of data mining based predictive maintenance often relies on extensive process and machine knowledge to enable appropriate feature selection and data preprocessing. Measurement data obtained may be asynchronous and result in inaccurate features, affecting the accuracy of maintenance prediction. To overcome this drawback, this paper introduces an approach to automatically select a feature subset through a genetic algorithm. The full feature set is created based on different sliding windows characterizing different time shifts on adopted statistical metrics of the measurement data. The fitness function of the genetic algorithm is then developed based on the preliminary fitting of a hidden Markov model (HMM) on the selected subset of features and assumed machines' condition in the training data. Ultimately the fittest subset of features is used to enable HMM based predictive maintenance. The proposed approach is evaluated using data from semi conductor wafer production equipment, recorded over a period of one year.
author_names: Jakob Kinghorst

Omid Geramifard

Ming Luo

Hian-Leng Chan

Khoo Yong

Jens Folmer

Minjie Zou

Birgit Vogel-Heuser
title: Hidden Markov model based predictive maintenance in semiconductor manufacturing: A genetic algorithm approach
venue: 2017 13th IEEE Conference on Automation Science and Engineering (CASE)
year: 2017

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

author_names: Jimmy Iskandar

James R Moyne

K Subrahmanyam

Parris C M Hawkins

Michael Armacost
title: Predictive Maintenance in Semiconductor Manufacturing Moving to Fab Wide Solutions
year: 2015

abstract: Predictive Maintenance methods are aimed to obtain reliable estimates of the remaining life cycle of an equipment from time series of suitable process parameters, named "health factors" typically exhibiting a monotone evolution associated with the equipment wear. The present study was motivated by the predictive maintenance of a dry etching equipment within a semiconductor manufacturing process. The optimal prediction of the health factor, represented by the cooling helium flow, must cope with noisy measurements of the health factor (possibly masking its monotonicity) and non uniform sampling times. The problem is formulated as a stochastic filtering problem in which a stochastic process has to be optimally predicted based on noisy and irregularly sampled observations. In particular, a hidden Gamma process model is proposed in order to capture all the features of the health factor, namely its nonnegativity and nonnegativity of its derivative. Since this filtering problem is not amenable to a closed form solution, a numerical Monte Carlo approach based on particle filtering is developed. Additionally, an adaptive parameter identification procedure is proposed to achieve the best trade off between promptness and noise insensitivity.
author_names: Andrea Schirru

Simone Pampuri

Giuseppe De Nicolao
title: Particle filtering of hidden Gamma processes for robust Predictive Maintenance in semiconductor manufacturing
venue: 2010 IEEE International Conference on Automation Science and Engineering
year: 2010

abstract: The present paper is motivated by the application of Predictive Maintenance (PM) techniques in the semiconductor manufacturing environment: such techniques are able, using process data, to make reliable predictions of residual equipment lifetime. The employment of PM yields positive fallouts on the productive process in form of unscheduled downtime reduction, increased spare parts availability and improved overall production quality. One of the main challenges in PM modeling regards the data driven assessment of relevant process variables when insufficient expert knowledge is available. In this paper, survival models theory is employed jointly with l1 penalization techniques: this allows to obtain sparse models able to select the meaningful process variables and simultaneously predict the remaining lifetime of an equipment. Additionally, frailty modeling techniques are employed to concurrently handle several productive equipments of the same type, exploiting their similarities to increase prediction accuracy. The proposed methodology is validated, illustrating promising results, by means of a semiconductor manufacturing dataset.
author_names: Simone Pampuri

Andrea Schirru

Cristina De Luca

Giuseppe De Nicolao
title: Proportional hazard model with l1 Penalization applied to Predictive Maintenance in semiconductor manufacturing
venue: 2011 IEEE International Conference on Automation Science and Engineering
year: 2011

abstract: In this paper, the predictive maintenance(PdM) technology was applied to the semiconductor manufacturing equipment and the DBSCAN algorithm parameters were optimized. The data obtained from the semiconductor equipment was obtained using acceleration sensors and was used to determine the conditions of the equipment. To simulate the malfunction condition of the equipment, the virtual error data was artificially generated. The DBSCAN clustering algorithm is applied to both acceleration sensor data and virtual error data to trace the moving of the center point of each cluster. By tracing the center points, the appropriate time for equipment maintenance can be determined. Also, the parameters of the DBSCAN algorithm were optimized using one way factorial design to improve to determine the exact time of maintenance.
author_names: Heesu Yoon

Seung-Soo Han
title: Clustering Parameter Optimization of Predictive Maintenance Algorithm for Semiconductor Equipment Using One way Factorial Design
venue: 2019 19th International Conference on Control, Automation and Systems (ICCAS)
year: 2019

abstract: The goal of this work was to implement a wireless RFID based Sensor Network in the UHF frequency region inside a semiconductor cleaning machine to supplement existing sensors in order to simplify usability and reduce production costs. The sensors are chosen in a way to extract useful information about the status of the semiconductor production machine to invoke specific system maintenance, thereby reducing maintenance times and costs. The great challenges are to obtain passive UHF receiver tags and sensors that reliably have to be detected among hundreds of other sensors in an electromagnetically complex and chemically harsh environment using innovative packaging and novel integration technologies.
author_names: Thomas Moldaschl

Ales Travnik

A Frank

Jochen Bardong

Heinz Cramer

Herbert Kastberger

Matthias Weitlaner

Michael Jerne

Alexey Nazarov

Christina Hirschl
title: RFID based Wireless Sensor Network for Semiconductor Manufacturing Equipment to enable Predictive Maintenance
year: 2019

abstract: Semiconductor equipment is closely related to production yield and should be periodically inspected to maintain the performance. However, traditional equipment maintenance methods have difficulty responding to sudden failures and economic losses due to unpredicted situation. Recently, predictive maintenance technologies have been actively studied, with many advantages, such as the ability to predict the right time of equipment maintenance in advance to ensure system stability and maximize return on capital. Economic gains and increased equipment stability are particularly important in the semiconductor industry. In this paper, we developed a predictive maintenance technique for wafer transport robots. A technology was developed to predict the failure of the robot by tracing the median values of clusters of the frequency data, which were acquired using acceleration sensors attached on the handler of the robot. By applying this system to the wafer transport robot, the simulation results showed that the time of repair can be predicted in real time.
author_names: Hyeong-Gyun Kim

Heesu Yoon

Ji Hyun Yoo

Hyun-il Yoon

Seung-Soo Han
title: Development of Predictive Maintenance Technology for Wafer Transfer Robot using Clustering Algorithm
venue: 2019 International Conference on Electronics, Information, and Communication (ICEIC)
year: 2019