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 dif- ferent 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.
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