In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.
|Title of host publication||2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Publication status||Published - 19 Sep 2019|
|Event||15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada|
Duration: 22 Aug 2019 → 26 Aug 2019
|Name||IEEE International Conference on Automation Science and Engineering|
|Conference||15th IEEE International Conference on Automation Science and Engineering, CASE 2019|
|Period||22/08/2019 → 26/08/2019|
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Student thesis: Doctoral Thesis › Doctor of Philosophy