Optimal Tuning of Epitaxy Pyrometers

Epitaxy is a process strongly dependent on wafer temperature. Unfortunately, the performance of the pyrometers in charge of sensing wafer temperature deteriorate with the usage. This represents the major maintenance issue for epitaxy process engineers who have to frequently calibrate pyrometers emissivity coefficient. At the present state the change of the emissivity coefficient is heuristically based on fab tradition and process engineers experience. We present a statistical tool to map the relationship between change in the temperature readings and emissivity adjustments. The module has been tested on real industrial dataset

РЫНОК ПЕРЕСТРАХОВАНИЯ В ПЕРИОД ГЛОБАЛЬНОЙ РЕЦЕССИИ

Abstract: В статье исследован рынок перестрахования в период глобальной рецессии. Рассмотрен рынок перестрахования и его место в глобальном страховом пространстве. Рассмотрены сущность рынка перестрахования и особенности его развития. Выделены процессы капитализации на мировом рынке перестрахования, определен процесс становления рынка перестрахования и циклы его развития. Раскрыто современное состояние отечественного и иностранного рынка перестрахования и тенденции его развития в условиях глобальной рецессии. Раскрыт механизм функционирования глобальных перестраховщиков на рынке перестрахования. Раскрыто перспективы развития отечественного рынка перестрахования с учетом глобальных тенденций

Multistep virtual metrology for semiconductor manufacturing: A multilevel and regularization methods based approach.

In semiconductor manufacturing, wafer quality control strongly relies on product monitoring and physical metrology. However, the involved metrology operations, generally performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, in common practice a small subset only of a productive lot is measured at the metrology stations and it is devoted to represent the entire lot. Virtual Metrology (VM) methodologies are used to obtain reliable predictions of metrology results at process time, without actually performing physical measurements. This goal is usually achieved by means of statistical models and by linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a given wafer (such as layer thickness and critical dimensions) depend on the whole processing and not on the last step before measurement only. In this paper, we investigate the possibilities to enhance VM prediction accuracy by exploiting the knowledge collected in the previous process steps. We present two different schemes of multi-step VM, along with dataset preparation indications. Special emphasis is placed on regression techniques capable of handling high-dimensional input spaces. The proposed multi-step approaches are tested on industrial production data

Prediction of integral type failures in semiconductor manufacturing through classification methods

In this paper we present a model-based approach for designing efficient control strategies with the aim of increasing the performance of Heating, Ventilation and Air- Conditioning (HVAC) systems with ice Cold Thermal Energy Storage (ice CTES). The use of TES systems ensures reduced energy costs and energy consumption, increased flexibility of operation, reduced equipment size and pollutant emissions. A simulation environment based on Matlab/Simulink\uae is developed, where the thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid-melting-liquid, liquidfreezing- solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Three standard control strategies and a model predictive control approach are developed and compared. Extensive simulations confirm that the MPC provides the best control in terms of energy efficiency and cooling load demand satisfaction with respect to standard control strategies

A Predictive Maintenance System based on Regularization Methods for Ion-Implantation

Ion Implantation is one of the most sensitive processes in Semiconductor Manufacturing. It consists in impacting accelerated ions with a material substrate and is performed by an Implanter tool. The major maintenance issue of such tool concerns the breaking of the tungsten filament contained within the ion source of the tool. This kind of fault can happen on a weekly basis, and the associated maintenance operations can last up to 3 hours. It is important to optimize the maintenance activities by synchronizing the Filament change operations with other minor maintenance interventions. In this paper, a Predictive Maintenance (PdM) system is proposed to tackle such issue; the filament lifetime is estimated on a statistical basis exploiting the knowledge of physical variables acting on the process. Given the high-dimensionality of the data, the statistical modeling has been based on Regularization Methods: Lasso, Ridge Regression and Elastic Nets. The predictive performances of the aforementioned regularization methods and of the proposed PdM module have been tested on actual productive semiconductor data

Multistep Virtual Metrology Approaches for Semiconductor Manufacturing Processes

In semiconductor manufacturing, state of the art for wafer quality control relies on product monitoring and feedback control loops; the involved metrology operations are particularly cost-intensive and time-consuming. For this reason, it is a common practice to measure a small subset of a productive lot and devoted to represent the whole lot. Virtual Metrology (VM) methodologies are able to obtain reliable predictions of metrology results at process time; this goal is usually achieved by means of statistical models, linking process data and context information to target measurements. Since production processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a certain wafer (e.g. layer thickness, electrical test results) depend on the whole processing and not only on the last step before measurement. In this paper, we investigate the possibilities to improve the VM quality relying on knowledge collected from previous process steps. We will present two different scheme of multistep VM, along with dataset preparation indications; special consideration will be reserved to regression techniques capable of handling high dimensional input spaces. The proposed multistep approaches will be tested against actual data from semiconductor manufacturing industry