Guided preventive maintenance

M. Sorokina

Research output: ThesisPd Eng Thesis

Abstract

Currently, daily maintenance of the electron microscope for inexperienced users is a complicated and time consuming procedure. It becomes even more challenging in industrial environment, where better performance, less down time, higher throughput and lower cost-per-sample are essential.
Automation is expected to make the daily guided preventive maintenance (GPM) of the microscope easier and at the same time to keep the tool always in the same state. This report describes and evaluates the possibilities to automate the GPM procedure to a high extend. Further the report addresses the possibilities to make GPM scheduled and modularized. This is done by investigating the alignment parameters and their change over longer periods of time. Also the sensitivity of the results is tested to variations in the alignment parameters.
The microscope stability analysis was expected to provide an insight on tool behavior. The analysis was based on files, which were obtained after each GPM procedure. Such files contain alignment information. In order to ease data access and speed up the work, a python code was developed to cope with data processing. It gives an overview of historical data in the form of simple statistical tools as plots and histograms. None of the investigated parameters have shown clear trends or consistent incrementing over time. However it was possible to establish a range of optimal tool performance. It was driven from three sigma deviations of investigated parameters settings. These ranges were further used as an input for sensitivity tests.
Sensitivity tests were expected to define how strong the investigated parameters influence the metrology. As a result it shows when the user needs to implement corrective actions in order to keep metrology and tool performance optimal. Parameters, which refer to daily alignments, were tested to determine their stability and influence on image quality. The experiment was designed around feature recognition and the measurement of feature dimensions, which is currently highly used in the factories. This sequence allows investigating the metrology by measuring feature width and height. Selection of this time-proved method for sensitivity tests ensures high repeatability, known reliability, and connection to the industry. Standalone python code was developed for processing of the obtained experimental data and calculation of the quantitative process capability characteristic.
Based on experimental results eleven investigated parameters were divided into three groups. The first group contains two parameters with strongly influence the image quality; therefore, they should be maintained regularly. The second group consists of four parameters, which have not shown any influence on metrology in the suggested ranges. As long as these parameters are in the optimal range, they do not require realignment. For the third group of parameters the ranges, established in tool behavior analysis, proved to be incorrect. Due to various physical reasons the operating ranges for these parameters are narrower. Consequently these parameters require another reference range and further investigation.
The GPM procedure was automated for more than 90%. It includes not only direct alignments, but also automated measurement sequences. The developed recipe has a huge marketing potential. This software is evaluated by experts to be a great help for inexperienced users and was already released to the customers.
Original languageEnglish
Awarding Institution
Supervisors/Advisors
  • den Hollander, K., External supervisor, External person
  • Aerts, S., External supervisor, External person
  • Cottaar, E.J.E. (Ward), Supervisor
Award date14 Jun 2016
Place of PublicationEindhoven
Publisher
Publication statusPublished - 2016

Bibliographical note

PDEng thesis. - Confidential forever.

Fingerprint Dive into the research topics of 'Guided preventive maintenance'. Together they form a unique fingerprint.

  • Cite this

    Sorokina, M. (2016). Guided preventive maintenance. Technische Universiteit Eindhoven.