Gian Antonio Susto

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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)
Silicon Epitaxial Deposition is a process strongly influenced by wafer temperature behavior, that has to be constantly monitored to avoid the production of defective wafers. A Predictive Maintenance (PdM) System is here proposed with the aim of predicting process behavior and scheduling control actions in advance. Two different prediction techniques have(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 pre-dictive models. Moreover, feature extraction techniques usually make(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)
In semiconductor manufacturing plants, monitoring physical properties of all wafers is fundamental in order to maintain good yield and high quality standards. However, such an approach is too costly and in practice only few wafers in a lot are actually monitored. Virtual Metrology (VM) systems allow to partly overcome the lack of physical metrology. In a VM(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)
In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present(More)