Model Development of a Virtual Learning Environment to Enhance Lean Education

AbstractModern day industry is becoming leaner by the day. This demands engineers with an in-depth understanding of lean philosophies. Current methods for teaching lean include hands-on projects and simulation. However, simulation games available in the market lack simplicity, ability to store the results, and modeling power. The goal of this research is to develop a virtual simulation platform which would enable students to perform various experiments by applying lean concepts. The design addresses these deficiencies through the use of VE-Suite, a virtual engineering software. The design includes user-friendly dialogue boxes, graphical models of machines, performance display gauges, and an editable layout. The platform uses laws of operations management such as Little's law, economic order quantity (EOQ) models, and cycle time. These laws enable students to implement various lean concepts such as pull system, just-in-time (JIT), single piece flow, single minute exchange of dies (SMED), kaizen, kanban, U-layout, by modifying the process parameters such as process times, setup times, layout, number, and placement of machines. The simulation begins with a traditional push type mass production line and the students improve the line by implementing lean techniques. Thus, students experience the advantages of lean real time while facing the real life problems encountered in implementing it

Manufacturing Lead Time Estimation with the Combination of Simulation and Statistical Learning Methods

In the paper, a novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times. Prediction is made by setting up models using statistical learning methods (multivariate regression); trained, validated and tested on log data gathered by manufacturing execution systems (MES). Relevant features, i.e., the predictors most contributing to the response, are selected from a wider range of system parameters. The proposed method is tested on data provided by a discrete event simulation model (as a part of a simulation-based prediction framework) of a small-sized flow-shop system. Accordingly, log data are generated by simulation experiments, substituting the function of a MES system, while considering several different system settings (e.g., job arrival rate, test rejection rate). By inserting the prediction models into a simulation-based decision support system, prospective simulations anticipating near-future deviations and/or disturbances, could be supported. Consequently, simulation could be applied for reactive, disturbance-handling purposes, and, moreover, for training the prediction models. (C) 2015 The Authors. Published by Elsevier B.V

Development and Simulation Assessment of Semiconductor Production System Enhancements for Fast Cycle Times

Long cycle times in semiconductor manufacturing represent an increasing challenge for the industry and lead to a growing need of break-through approaches to reduce it. Small lot sizes and the conversion of batch processes to mini-batch or single-wafer processes are widely regarded as a promising means for a step-wise cycle time reduction. Our analysis with discrete-event simulation and queueing theory shows that small lot size and the replacement of batch tools with mini-batch or single wafer tools are beneficial but lot size reduction lacks persuasive effectiveness if reduced by more than half. Because the results are not completely convincing, we develop a new semiconductor tool type that further reduces cycle time by lot streaming leveraging the lot size reduction efforts. We show that this combined approach can lead to a cycle time reduction of more than 80%

EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that "learn" over time.Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Implementation; Evidence-Based Medicine; Data Warehouse; (2012). EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect. Health Policy and Technology. arXiv admin note: substantial text overlap with arXiv:1112.166

Wafer exchange simulation

ASML invents and develops complex technology for high-tech lithography, metrology and software solutions for the semiconductor industry. The technology consists of complex hardware that is controlled by complex software. For the key drivers, throughput, imaging, and overlay, testing the software is crucial. It also ensures availability, reliability, and robustness. Testing this software ranges from using the real hardware to simulators. Testing on real hardware is a challenge because of scarcity, testing dangerous situations etc. Hence it is important to improve the functionality of simulators. One such simulator is the Wafer Flow Simulator (WFS) that simulates the flow of a wafer in the Wafer Handling (WH) subsystem. The WH receives the wafer from the outside world and loads it to the Wafer Stages (WS) subsystem, which positions the wafer for measuring and exposing. The Wafer Exchange (WEX) specifies the protocol for transferring the wafer between the WH and WS. Currently the testing of the WEX software is executed after integration of these subsystems in the real hardware. This project provides an extension to the WFS to enable testing of the integrated software of the two subsystems in a software only environment, which is widely available and relatively cheap. The solution direction is to extend the WFS to describe and simulate the WS hardware required to execute the WEX. The result is that the WEX soft-ware can be tested before deploying the software to the real hardware, hence saving valuable machine time and increasing test coverage

Online Simulation in Semiconductor Manufacturing

In semiconductor manufacturing discrete event simulation systems are quite established to support multiple planning decisions. During the recent years, the productivity is increasing by using simulation methods. The motivation for this thesis is to use online simulation not only for planning decisions, but also for a wide range of operational decisions. Therefore an integrated online simulation system for short term forecasting has been developed. The production environment is a mature high mix logic wafer fab. It has been selected because of its vast potential for performance improvement. In this thesis several aspects of online simulation will be addressed: The first aspect is the implementation of an online simulation system in semiconductor manufacturing. The general problem is to achieve a high speed, a high level of detail, and a high forecast accuracy. To resolve these problems, an online simulation system has been created. The simulation model has a high level of detail. It is created automatically from underling fab data. To create such a simulation model from fab data, additional problems related to the underlying data arise. The major parts are the data access, the data integration, and the data quality. These problems have been solved by using an integrated data model with several data extraction, data transformation, and data cleaning steps. The second aspect is related to the accuracy of online simulation. The overall problem is to increase the forecast horizon, increase the level of detail of the forecast and reduce the forecast error. To provide useful forecast results, the simulation model contains a high level of modeling details and a proper initialization. The influences on the forecast quality will be analyzed. The results show that the simulation forecast accuracy achieves good quality to predict future fab performance. The last aspect is to find ways to use simulation forecast results to improve the fab performance. Numerous applications have been identified. For each application a description is available. It contains the requirements of such a forecast, the decision variables, and background information. An application example shows, where a performance problem exists and how online simulation is able to resolve it. To further enhance the real time capability of online simulation, a major part is to investigate new ways to connect the simulation model with the wafer fab. For fab driven simulation, the simulation model and the real wafer fab run concurrently. The wafer fab provides several events to update the simulation during runtime. So the model is always synchronized with the real fab. It becomes possible to start a simulation run in real time. There is no further delay for data extraction, data transformation and model creation. A prototype for a single work center has been implemented to show the feasibility

A framework for generating operational characteristic curves for semiconductor manufacturing systems using flexible and reusable discrete event simulations

This thesis proposes a framework for generating operating curves for semiconductor manufacturing facilities using a modular flexible discrete event simulation (DES) model embedded in an application that automates the design of experiments for the simulations. Typically, operating curves are generated using analytical queueing models that are difficult to implement and hence, can only be used for benchmarking purposes. Alternatively, DES models are more capable of capturing the complexities of a semiconductor manufacturing facility such as re-entrancy, rework and non-identical toolsets. However, traditional craft-based simulations require much time and resources. The proposed methodology aims to reduce this time by automatically calculating the parameters for experimentation and generating the simulation model. It proposes a novel method to more appropriately allocate simulation effort by selecting design points more relevant to the operating curve. The methodology was initially applied to a single toolset model and tested as a pilot case study using actual factory data. Overall, the resulting operating curves matched that of the actual data. Subsequently, the methodology was applied to a full semiconductor manufacturing facility, using datasets from the Semiconductor Wafer Manufacturing Data Format Specification. The automated framework was shown to generate the curves rapidly and comparisons against a number of queueing model equivalents showed that the DES curves were more accurate. The implications of this work mean that on deployment of the application, semiconductor manufacturers can quickly obtain an accurate operating curve of their factory that could be used to aid in capacity planning and enable better decision-making regarding allocation of resources