Modeling the effect of hot lots in semiconductor manufacturing systems

The presence of hot lots or high-priority jobs in semiconductor manufacturing systems is known to significantly affect the cycle time and throughput of the regular lots since the hot lots get priority at all stages of processing. In this paper, we present an efficient analytical model based on re-entrant lines and use an efficient, approximate analysis methodology for this model in order to predict the performance of a semiconductor manufacturing line in the presence of hot lots. The proposed method explicitly models scheduling policies and can be used for rapid performance analysis. Using the analytical method and also simulation, we analyze two re-entrant lines, including a full-scale model of a wafer fab, under various buffer priority scheduling policies. The numerical results show the severe effects hot lots can have on the performance characteristics of regular lots

Modelling and simulation of advanced semiconductor devices

This paper presents a modelling and simulation study of advanced semiconductor devices. Different Technology Computer Aided Design approaches and models, used in nowadays research are described here. Our discussions are based on numerous theoretical approaches starting from first principle methods and continuing with discussions based on more well stablished methods such as Drift-Diffusion, Monte Carlo and Non-Equilibrium Green’s Function formalism

Aerospace Manufacturing Industry: A Simulation-Based Decision Support Framework for the Scheduling of Complex Hoist Lines

The hoist scheduling problem is a critical issue in the design and control of Automated Manufacturing Systems. To deal with the major complexities appearing in such problem, this work introduces an advanced simulation model to represent the short-term scheduling of complex hoist lines. The aim is to find the best jobs schedule that minimizing the makespan while maximizing throughput with no defective outputs. Several hard constraints are considered in the model: single shared hoist, heterogeneous recipes, eventual recycles flows, and no buffers between workstations. Different heuristic-based strategies are incorporated into the computer model in order to improve the solutions generated over time. The alternative solutions can be quickly evaluated by using a graphical user interface developed together with the simulation model.Fil: Basán, Natalia Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Pulido, Raul. Universidad Politécnica de Madrid; EspañaFil: Coccola, Mariana Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Aggregate modeling in semiconductor manufacturing using effective process times

In modern manufacturing, model-based performance analysis is becoming increasingly important due to growing competition and high capital investments. In this PhD project, the performance of a manufacturing system is considered in the sense of throughput (number of products produced per time unit), cycle time (time that a product spends in a manufacturing system), and the amount of work in process (amount of products in the system). The focus of this project is on semiconductor manufacturing. Models facilitate in performance improvement by providing a systematic connection between operational decisions and performance measures. Two common model types are analytical models, and discrete-event simulation models. Analytical models are fast to evaluate, though incorporation of all relevant factory-fl oor aspects is difficult. Discrete-event simulation models allow for the inclusion of almost any factory-fl oor aspect, such that a high prediction accuracy can be achieved. However, this comes at the cost of long computation times. Furthermore, data on all the modeled aspects may not be available. The number of factory-fl oor aspects that have to be modeled explicitly can be reduced signiffcantly through aggregation. In this dissertation, simple aggregate analytical or discrete-event simulation models are considered, with only a few parameters such as the mean and the coeffcient of variation of an aggregated process time distribution. The aggregate process time lumps together all the relevant aspects of the considered system, and is referred to as the Effective Process Time (EPT) in this dissertation. The EPT may be calculated from the raw process time and the outage delays, such as machine breakdown and setup. However, data on all the outages is often not available. This motivated previous research at the TU/e to develop algorithms which can determine the EPT distribution directly from arrival and departure times, without quantifying the contributing factors. Typical for semiconductor machines is that they often perform a sequence of processes in the various machine chambers, such that wafers of multiple lots are in process at the same time. This is referred to as \lot cascading". To model this cascading behavior, in previous work at the TU/e an aggregate model was developed in which the EPT depends on the amount of Work In Process (WIP). This model serves as the starting point of this dissertation. This dissertation presents the efforts to further develop EPT-based aggregate modeling for application in semiconductor manufacturing. In particular, the dissertation contributes to: dealing with the typically limited amount of available data, modeling workstations with a variable product mix, predicting cycle time distributions, and aggregate modeling of networks of workstations. First, the existing aggregate model with WIP-dependent EPTs has been extended with a curve-fitting approach to deal with the limited amount of arrivals and departures that can be collected in a realistic time period. The new method is illustrated for four operational semiconductor workstations in the Crolles2 semiconductor factory (in Crolles, France), for which the mean cycle time as a function of the throughput has been predicted. Second, a new EPT-based aggregate model that predicts the mean cycle time of a workstation as a function of the throughput, and the product mix has been developed. In semiconductor manufacturing, many workstations produce a mix of different products, and each machine in the workstation may be qualified to process a subset of these products only. The EPT model is validated on a simulation case, and on an industry case of an operational Crolles2 workstation. Third, the dissertation presents a new EPT-based aggregate model that can predict the cycle time distribution of a workstation instead of only the mean cycle time. To accurately predict a cycle time distribution, the order in which lots are processed is incorporated in the aggregate model by means of an overtaking distribution. An extensive simulation study and an industry case demonstrate that the aggregate model can accurately predict the cycle time distribution of integrated processing workstations in semiconductor manufacturing. Finally, aggregate modeling of networks of semiconductor workstations has been explored. Two modeling approaches are investigated: the entire network is modeled as a single aggregate server, and the network is modeled as an aggregate network that consists of an aggregate model for each workstation. The accuracy of the model predictions using the two approaches is investigated by means of a simulation case of a re-entrant ow line. The results of these aggregate models are promising

Remediation of contaminated marine sediment using bentonite, kaolin and sand as capping materials

There is a growing public concern over the issue of sediment contamination resulting from industrial, municipal wastewater, mining activities, and improper use of chemical fertilizer or pesticides. The conventional treatment of contaminated sediment is dredging, but this treatment is expensive and requires a large area of land for disposal. In situ capping of contaminated sediment is considered as a cheaper technique compared to dredging and efficient treatment technology to immobilize pollutants in sediments on site. In this technique, sediments are capped by placing a layer of inert materials like sand, clean soil, or gravel or active materials like activated carbon, zeolite, or apatite over sediments in order to reduce the risk to the aquatic environment. The objective of this study is to determine the effectiveness of using active materials; bentonite (B), kaolin (K), mixture of bentonite with kaolin (1:1) (BK) as capping materials to block the release of five heavy metals (Pb, Cr, Cu, Cd and Zn) from artificially polluted sediments. The effectiveness of B, K, and BK for preventing the leachability of the trace metals was assessed on a bench-scale laboratory experiment in glass tanks for 90 days, where 1cm thick layer of capping material and sand was placed above the contaminated sediment. The results showed that B and BK reduced the leachability of Pb, Cr, and Cu from the sediments. The results also showed that B and BK could be used as potential capping materials for the remediation of contaminated sites due to their significant entrapping of Pb, Cu, and Cr. The pollutants were released into the overlying water from the contaminated sediment in the following decreasing order; Cd > Zn > Pb > Cu > Cr. The adsorption kinetics analysis also showed that the process of adsorption was by chemisorption. This study proved that bentonite and mixture of bentonite with kaolin clays covered with sand could be used as capping materials for in situ treatment of Pb, Cu, Cr, Zn, and Cd for contaminated marine sediment