Taesu Cheong

Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing

A method of fault detection and classification (FDC) for semiconductor manufacturing equipment e-diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D-S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge.

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In this paper, we study the optimal policies of retailers who operate their inventory with a single period model (i.e., newsvendor model) under a free shipping offer where a fixed shipping fee is exempted if an order quantity is greater than or equal to a given minimum quantity. Zhou et al. (2009) have explored this model, and we further investigate their analysis for the optimal ordering policies which they did not sufficiently develop. Based on the investigation, we extend the base model in order to deal with the practically important aspect of inventory management when the exact distribution function of demand is not available. We incorporate the aspect into the base model and present the optimal policies for the extended model with a numerical example. Finally, we conduct extensive numerical experiments to evaluate the performance of the extended model and analyze the impacts of minimum free shipping quantity and the fixed shipping fee on the performance.

-Diagnostics

Radio Frequency Identification (RFID) middleware is a new breed of software system which facilitates data communication between automatic identification equipments like RFID readers and enterprise applications. It provides a distributed environment to process the data coming from tags, filter and then deliver it to a variety of backend applications via various communication protocols including web services. In this paper, we focus on the information flow converting raw RFID data to useful information which may even be used to lead to automated business process execution and further to knowledge to support decision making, and we define the information flow as so-called ‘RFID Information Value Chain (RFID IVC)’. We examine the elements and associated activities of RFID IVC and also introduce the RFID middleware ecosystem not only to provide the seamless environment spanning from the edge of the enterprise network to the enterprise systems, but also to support the activities arisen on RFID IVC. RFID middleware ecosystem consists of RFID middleware, rule engine to generate business semantic events and orchestration engine to coordinate sort of business process invoked by RFID tag data capture event. Moreover, the implementations of each system residing in RFID middleware ecosystem are introduced and the relationship between RFID middleware ecosystem and RFID IVC is demonstrated.

A minimax distribution-free procedure for a newsvendor problem with free shipping

We investigate the value of choosing the next stop to visit in a multistop trip based on current traffic conditions to minimize the expected total travel time of the tour. We model this problem as a Markov decision process. Numerical examples are presented that indicate that the value of using current traffic information for dynamic tour determination can be significant.

RFID data management and RFID information value chain support with RFID middleware platform implementation

Radio frequency identification (RFID) middleware is a new breed of software system which facilitates data communication between automatic identification equipments like RFID readers and enterprise applications. It provides a distributed environment to process the data coming from tags, filter and then deliver it to a variety of backend applications via various communication protocols including Web services. In this paper, we demonstrate the implementation of the RFID middleware system that is not only compatible with EPCglobal ALE (application level events) specification but also provides various types of RFID readers spanning from passive readers to active readers. Overall functional components of the middleware software as well as the system framework are illustrated

Dynamic Traveling Salesman Problem: Value of Real-Time Traffic Information

With the expansion of the Internet and the proliferation of e-businesses, many manufacturers have chosen to distribute products through online retail channels in addition to brick-and-mortar retail channels. In this paper, we consider a dual-channel supply chain in which a manufacturer considers selling a product through a conventional retail channel and an online channel. Considering two common procurement and order fulfillment policies for online retailers (etailers), conventional batch ordering and drop-shipping, we investigate the impact of supply chain power structure in terms of market power and retail channel dominance on a manufacturer’s optimal distribution channel strategy. We analyze and compare the two procurement models with respect to the etailer’s order fulfillment policies. We find that a manufacturer never prefers a drop-shipping etailer as the first mover in a sequential pricing game with a batch ordering traditional retailer. The manufacturer prefers to award the power of retail channel dominance to a batch ordering retailer with relatively high market power even if the etailer also follows a batch ordering policy. Drop-shipping benefits both the manufacturer and the etailer when the etailer has relatively low market power.

REMS and RBPTS: ALE-compliant RFID Middleware Software Platform

For decades, RFID technology has been progressed as various types like EAS, passive RFID, active RFID, smart label, smart card, etc for their effectiveness in security as well as cost. RFID technology can be adopted for various applications from simple identification for item management to complex track and trace system for container management. In this paper, freight container yard management system using RFID technology is introduced. It consists of RFID devices, middleware and applications. RFID devices used for this system contain tags and readers with electronic seal technology as well as passive, active RFID technology. Passive tag is used for trailer truck identification while active tag for transportation information. Electronic seal tag is to be attached on the container for container identification and security.

Impact of supply chain power and drop-shipping on a manufacturer’s optimal distribution channel strategy

This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.

Freight Container Yard Management System with Electronic Seal Technology

We investigate how the choice of coopetition of the simultaneous pursuit of collaboration and competition dynamically impacts both the participating firms and also the other self-developing ones in the same market. A conceptual framework of mathematical models obtained from the arguments and insights in the literature is used to undertake an in-depth study through a multiperiod analysis from 2013 to 2020 of an exemplar case of coopetition, the two concurrently ongoing coopetition partnerships in the US electric vehicle (EV) market, the Tesla Motors-Daimler AG alliance and the Tesla Motors-Toyota alliance and the other firms which are not involved in coopetition.

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

This article investigates the impact of information discrepancy between a drop-shipper and an online retailer on the drop-shipping supply chain performance. The inventory information misalignment between them contributes to the failure of order fulfillment and demand satisfaction, and hence the associated penalties are incurred. In this article, we first analyze the penalties of ignoring such information discrepancy on both the drop-shipper and the online retailer. We then assess the impact of information discrepancy on both parties when the drop-shipper understands the existence of the information discrepancy but is not able to eliminate the errors. The numerical experiments indicate that both parties can have significant amount of the percentage cost reductions if the information discrepancy can be eliminated, and the potential savings are substantial especially when the errors have large variability. Furthermore, we observe that the online retailer is more vulnerable to information discrepancy than the drop-shipper, and the drop-shipper is likely to suffer from the online retailers underestimation of the physical inventory level more than the problem of its overestimation. Moreover, even if eliminating errors is not possible, both parties could still benefit from taking the possibility of errors into consideration in decision making.