Simone Pampuri

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In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called “health factors,” or quantitative(More)
In semiconductor manufacturing, the purpose of chamber matching is the alignment of process and yield results of distinct chambers performing in parallel the same process step on different silicon wafers. In this paper, multi-level linear models and statistical process control techniques are jointly employed to define control charts for monitoring chamber(More)
In semiconductor manufacturing, the state of the art for wafer quality control is based on product monitoring and feedback control loops; the related metrology operations, that usually involve scanning electron microscopes, are particularly cost-intensive and time-consuming. It is therefore not possible to evaluate every wafer: commonly, a small subset of a(More)
In semiconductor manufacturing, Virtual Metrology (VM) methodologies aim to obtain reliable estimates of process results without actually performing measurement operations, that are cost-intensive and time-consuming. This goal is usually achieved by means of statistical models, linking (easily collectible) process data to target measurements. In this paper,(More)
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(More)
Virtual Metrology (VM) and soft sensing modules have become popular in the past years and are now widely adopted in semiconductor plants. Nevertheless, few scientific works have so far investigated interactions between VM and Run-to-Run (R2R), the most common control approach in the field. In this paper, a novel strategy aimed at integrating VM and R2R(More)
Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make(More)
Predictive Maintenance methods are aimed to obtain reliable estimates of the remaining life cycle of an equipment from time series of suitable process parameters, named “health factors”, typically exhibiting a monotone evolution associated with the equipment wear. The present study was motivated by the predictive maintenance of a dry etching(More)
Semiconductor manufacturing is one of the most technologically advanced industrial sectors. Process quality and control are critical for decreasing costs and increasing yield. The contribution of automatic control and statistical modeling in this area can drastically impact production performance. For this reason in the past decade major collaborative(More)