Chen-Fu Chien | National Tsing Hua University (original) (raw)

Papers by Chen-Fu Chien

Research paper thumbnail of 「工業工程與系統管理之回顧與展望」特刊：序言

Research paper thumbnail of Taiwan Drought was a Microcosm of Climate Change Adaptation Challenges in Complex Island Economies

Process integration and optimization for sustainability, Aug 31, 2021

Research paper thumbnail of Solid waste management in emerging economies: Opportunities and challenges for reuse and recycling

Resources Conservation and Recycling, Sep 1, 2021

Research paper thumbnail of Modelling and Decision Support System for Intelligent Manufacturing: An Empirical Study for Feedforward-Feedback Learning-Based Run-To-Run Controller for Semiconductor Dry-Etching Process

This study aims to address the formation of various decision models based on modeling, analytics,... more This study aims to address the formation of various decision models based on modeling, analytics, and optimization techniques for intelligent manufacturing and smart production. Indeed, smart manufacturing deals with flexible decisions for addressing the dynamic, competitive, and global supply chains and production networks by employing advanced information technology, intelligent computerized control, digital decision technologies, and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, this study provides a good snapshot of current quantitative modeling approaches, issues, and trends. The validity of the proposed framework is estimated with a number of illustrations. This study concludes with discussion of future research directions

Research paper thumbnail of Manufacturing intelligence and smart production for industry 3.5 and empirical study of decision-based virtual metrology for controlling overlay errors

2016 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), 2016

Focusing on the challenges of wafer fabs with multiple tools and high-mix products, this study ai... more Focusing on the challenges of wafer fabs with multiple tools and high-mix products, this study aims to propose a decision-based virtual metrology framework to reduce the metrology cost and enhance productivity. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of proposed approach for controlling overlay errors in lithography processes while ensuring the quality level. The results have shown practical viability of the proposed approach for reducing sampling rate and metrology cost.

Research paper thumbnail of A Data Mining and Time Series Integrated Approach for Analyzing Semiconductor MES and FDC Data to Enhance Overall Usage Effectiveness

Research paper thumbnail of Industry 3.5: IE developments and prospects in the Asia-pacific region

Research paper thumbnail of Empirical Study of Multi-Objective Parameter Optimization in Wire Bonding Process

2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), 2019

Wire bonding is one of the key processes which connects the wafer die and the lead frame in the I... more Wire bonding is one of the key processes which connects the wafer die and the lead frame in the IC packaging process. It is noted that process parameters influence both the speed and quality of bonding. Parameters, such as descending speed and cycles affect the bonding speed; while parameters such as bonding force and ultrasonic energy level affect the strength of joint. Both situations will affect the output (unit per hour, UPH), and lead to the variation of the production capacity as well.Nowadays, most companies in Taiwan still follow the rule of thumb when setting the process parameters. However, the large number of parameters in the bonding process makes it difficult for engineers to find appropriate parameters at once efficiently based on their experience only. When new products are introduced into mass production, engineers must go through multiple adjustments in a repeated trial and error way to find out the appropriate parameters, which takes lots of time, manpower and material resources.Using data mining techniques to establish predictive models for process parameters can not only increase the production capacity but also reduce the cost of the operation during adjustments. However, most studies in the field of wire bonding parameter optimization focused on increasing the bonding quality, while little consider the operation time and quality simultaneously. In this study, a data mining framework is proposed to extract the relationship between the bonding speed, bonding quality and the parameters.This study aims to increase production capacity by enhancing the bonding quality and the bonding speed simultaneously via recommending optimized process parameters. The proposed framework is based on a defect classification model constructed by random forest (RF) and extreme gradient boosting (XGBoost) method, and a multi-objective process parameters optimization model applying particle swarm optimization (PSO) method.An empirical study was conducted in a leading IC packaging and assembly company in Taiwan. The empirical result on 2 testing products reveals that the proposed approach increases the bonding quality by 70%, whereas the bonding speed is enhanced by 20%; overall, the proposed approach benefits output UPH by 26.6%, 5.0% for each product. Besides improving the production capacity, the proposed framework can also recommend parameters systematically and more quickly to enhance engineers’ parameter tuning efficiency on-line and achieve the goal of reducing the introduction time of new products.

Research paper thumbnail of The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview

Research paper thumbnail of A UNISON framework for knowledge management of university–industry collaboration and an illustration

Computers & Industrial Engineering, 2019

Abstract Knowledge is a core competency for maintaining competitive advantages of organizations. ... more Abstract Knowledge is a core competency for maintaining competitive advantages of organizations. While universities conduct research and create knowledge, few studies have been conducted to address the applications of knowledge management (KM) to higher education in light of the increasing requirement for universities to play additional roles in the modern society. This study aims to develop a UNISON framework for managing knowledge for university–industry collaboration (UIC) with a plan-do-check-act (PDCA) improvement circle. An empirical study was conducted in Taiwan for improving employee productivity, optimizing resource use, motivating employees, and realizing continuous improvement. The results have shown practical viability of the proposed KM models to improve the performance of UIC as well as strengthen the efficiency and operational excellence for the university serving various social responsibilities. Indeed, the case study university, National Tsing Hua University, becomes the first national university being awarded for the National Quality Award in Taiwan. This study concludes with the contributions and discussions of future research directions.

Research paper thumbnail of Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing

IEEE Transactions on Semiconductor Manufacturing, 2018

Research paper thumbnail of How Smart Manufacturing Can Help Combat the COVID-19 Pandemic

Diagnostics, May 17, 2021

Research paper thumbnail of Semiconductor capacity expansion based on forecast evolution and mini-max regret strategy for smart production under demand uncertainty

Computers & Industrial Engineering, Mar 1, 2023

Research paper thumbnail of Strategic capacity planning for smart production: Decision modeling under demand uncertainty

Applied Soft Computing, Jul 1, 2018

Research paper thumbnail of Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

International Journal of Production Research, Dec 5, 2013

The re-entrant flow shop scheduling problem considering time windows constraint is one of the mos... more The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm | fmls, rcrc, temp | Cmax problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.

Research paper thumbnail of Special issue on recent advance in Intelligent Manufacturing Systems

Computers & Industrial Engineering, May 1, 2013

ABSTRACT Effective supply chain management (SCM) comprises activities involving the demand and su... more ABSTRACT Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent ...

Research paper thumbnail of Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

IEEE Transactions on Semiconductor Manufacturing

Research paper thumbnail of Constructing a Metrology Sampling Framework for In-line Inspection in Semiconductor Fabrication

Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, 2018

Research paper thumbnail of Performance of pre-production band 1 receiver for the Atacama Large Millimeter/submillimeter Array (ALMA)

Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy IX, 2018

The Atacama Large Millimeter/submillimeter Array (ALMA) Band 1 receiver covers the frequency rang... more The Atacama Large Millimeter/submillimeter Array (ALMA) Band 1 receiver covers the frequency range of 35-50 GHz. An extension of up to 52 GHz is on a best-effort basis. A total of 73 units have to be built in two phases: 8 preproduction and then 65 production units. This paper reports on the assembly, testing, and performance of the preproduction Band 1 receiver. The infrastructure, integration, and evaluation of the fully-assembled Band 1 receiver system will be covered. Finally, a discussion of the technical and managerial challenges encountered for this large number of receivers will be presented.

Research paper thumbnail of A UNISON Decision Analysis Framework Model and Its Empirical Studies

This study aims to develop the UNISON Framework as a systematic approach for decision analysis th... more This study aims to develop the UNISON Framework as a systematic approach for decision analysis that can assist the decision maker in the comprehensive processes for defining and structuring the right decision problem,clarifying the decision elements,collecting the data,extracting useful information,evaluating the alternatives to select the optimal decision for effective decision support.The proposed UNISON framework can enhance the decision quality of complex decision problems with justification.In this paper,the six phases of UNISON framework are explained in details and specific case studies are used to illustrate its validity.

Research paper thumbnail of 「工業工程與系統管理之回顧與展望」特刊：序言

Research paper thumbnail of Taiwan Drought was a Microcosm of Climate Change Adaptation Challenges in Complex Island Economies

Process integration and optimization for sustainability, Aug 31, 2021

Research paper thumbnail of Solid waste management in emerging economies: Opportunities and challenges for reuse and recycling

Resources Conservation and Recycling, Sep 1, 2021

Research paper thumbnail of Modelling and Decision Support System for Intelligent Manufacturing: An Empirical Study for Feedforward-Feedback Learning-Based Run-To-Run Controller for Semiconductor Dry-Etching Process

This study aims to address the formation of various decision models based on modeling, analytics,... more This study aims to address the formation of various decision models based on modeling, analytics, and optimization techniques for intelligent manufacturing and smart production. Indeed, smart manufacturing deals with flexible decisions for addressing the dynamic, competitive, and global supply chains and production networks by employing advanced information technology, intelligent computerized control, digital decision technologies, and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, this study provides a good snapshot of current quantitative modeling approaches, issues, and trends. The validity of the proposed framework is estimated with a number of illustrations. This study concludes with discussion of future research directions

Research paper thumbnail of Manufacturing intelligence and smart production for industry 3.5 and empirical study of decision-based virtual metrology for controlling overlay errors

2016 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), 2016

Focusing on the challenges of wafer fabs with multiple tools and high-mix products, this study ai... more Focusing on the challenges of wafer fabs with multiple tools and high-mix products, this study aims to propose a decision-based virtual metrology framework to reduce the metrology cost and enhance productivity. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of proposed approach for controlling overlay errors in lithography processes while ensuring the quality level. The results have shown practical viability of the proposed approach for reducing sampling rate and metrology cost.

Research paper thumbnail of A Data Mining and Time Series Integrated Approach for Analyzing Semiconductor MES and FDC Data to Enhance Overall Usage Effectiveness

Research paper thumbnail of Industry 3.5: IE developments and prospects in the Asia-pacific region

Research paper thumbnail of Empirical Study of Multi-Objective Parameter Optimization in Wire Bonding Process

2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), 2019

Wire bonding is one of the key processes which connects the wafer die and the lead frame in the I... more Wire bonding is one of the key processes which connects the wafer die and the lead frame in the IC packaging process. It is noted that process parameters influence both the speed and quality of bonding. Parameters, such as descending speed and cycles affect the bonding speed; while parameters such as bonding force and ultrasonic energy level affect the strength of joint. Both situations will affect the output (unit per hour, UPH), and lead to the variation of the production capacity as well.Nowadays, most companies in Taiwan still follow the rule of thumb when setting the process parameters. However, the large number of parameters in the bonding process makes it difficult for engineers to find appropriate parameters at once efficiently based on their experience only. When new products are introduced into mass production, engineers must go through multiple adjustments in a repeated trial and error way to find out the appropriate parameters, which takes lots of time, manpower and material resources.Using data mining techniques to establish predictive models for process parameters can not only increase the production capacity but also reduce the cost of the operation during adjustments. However, most studies in the field of wire bonding parameter optimization focused on increasing the bonding quality, while little consider the operation time and quality simultaneously. In this study, a data mining framework is proposed to extract the relationship between the bonding speed, bonding quality and the parameters.This study aims to increase production capacity by enhancing the bonding quality and the bonding speed simultaneously via recommending optimized process parameters. The proposed framework is based on a defect classification model constructed by random forest (RF) and extreme gradient boosting (XGBoost) method, and a multi-objective process parameters optimization model applying particle swarm optimization (PSO) method.An empirical study was conducted in a leading IC packaging and assembly company in Taiwan. The empirical result on 2 testing products reveals that the proposed approach increases the bonding quality by 70%, whereas the bonding speed is enhanced by 20%; overall, the proposed approach benefits output UPH by 26.6%, 5.0% for each product. Besides improving the production capacity, the proposed framework can also recommend parameters systematically and more quickly to enhance engineers’ parameter tuning efficiency on-line and achieve the goal of reducing the introduction time of new products.

Research paper thumbnail of The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview

Research paper thumbnail of A UNISON framework for knowledge management of university–industry collaboration and an illustration

Computers & Industrial Engineering, 2019

Abstract Knowledge is a core competency for maintaining competitive advantages of organizations. ... more Abstract Knowledge is a core competency for maintaining competitive advantages of organizations. While universities conduct research and create knowledge, few studies have been conducted to address the applications of knowledge management (KM) to higher education in light of the increasing requirement for universities to play additional roles in the modern society. This study aims to develop a UNISON framework for managing knowledge for university–industry collaboration (UIC) with a plan-do-check-act (PDCA) improvement circle. An empirical study was conducted in Taiwan for improving employee productivity, optimizing resource use, motivating employees, and realizing continuous improvement. The results have shown practical viability of the proposed KM models to improve the performance of UIC as well as strengthen the efficiency and operational excellence for the university serving various social responsibilities. Indeed, the case study university, National Tsing Hua University, becomes the first national university being awarded for the National Quality Award in Taiwan. This study concludes with the contributions and discussions of future research directions.

Research paper thumbnail of Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing

IEEE Transactions on Semiconductor Manufacturing, 2018

Research paper thumbnail of How Smart Manufacturing Can Help Combat the COVID-19 Pandemic

Diagnostics, May 17, 2021

Research paper thumbnail of Semiconductor capacity expansion based on forecast evolution and mini-max regret strategy for smart production under demand uncertainty

Computers & Industrial Engineering, Mar 1, 2023

Research paper thumbnail of Strategic capacity planning for smart production: Decision modeling under demand uncertainty

Applied Soft Computing, Jul 1, 2018

Research paper thumbnail of Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

International Journal of Production Research, Dec 5, 2013

The re-entrant flow shop scheduling problem considering time windows constraint is one of the mos... more The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm | fmls, rcrc, temp | Cmax problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.

Research paper thumbnail of Special issue on recent advance in Intelligent Manufacturing Systems

Computers & Industrial Engineering, May 1, 2013

ABSTRACT Effective supply chain management (SCM) comprises activities involving the demand and su... more ABSTRACT Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent ...

Research paper thumbnail of Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

IEEE Transactions on Semiconductor Manufacturing

Research paper thumbnail of Constructing a Metrology Sampling Framework for In-line Inspection in Semiconductor Fabrication

Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, 2018

Research paper thumbnail of Performance of pre-production band 1 receiver for the Atacama Large Millimeter/submillimeter Array (ALMA)

Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy IX, 2018

The Atacama Large Millimeter/submillimeter Array (ALMA) Band 1 receiver covers the frequency rang... more The Atacama Large Millimeter/submillimeter Array (ALMA) Band 1 receiver covers the frequency range of 35-50 GHz. An extension of up to 52 GHz is on a best-effort basis. A total of 73 units have to be built in two phases: 8 preproduction and then 65 production units. This paper reports on the assembly, testing, and performance of the preproduction Band 1 receiver. The infrastructure, integration, and evaluation of the fully-assembled Band 1 receiver system will be covered. Finally, a discussion of the technical and managerial challenges encountered for this large number of receivers will be presented.

Research paper thumbnail of A UNISON Decision Analysis Framework Model and Its Empirical Studies

This study aims to develop the UNISON Framework as a systematic approach for decision analysis th... more This study aims to develop the UNISON Framework as a systematic approach for decision analysis that can assist the decision maker in the comprehensive processes for defining and structuring the right decision problem,clarifying the decision elements,collecting the data,extracting useful information,evaluating the alternatives to select the optimal decision for effective decision support.The proposed UNISON framework can enhance the decision quality of complex decision problems with justification.In this paper,the six phases of UNISON framework are explained in details and specific case studies are used to illustrate its validity.