Electrostatic Lens System Design and Optimization Using Evolutionary Algorithms

Research output: ThesisDissertation (TU Delft)

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Abstract

The design of electrostatic charged particle lenses involves changing many geometrical parameters of the lens electrodes as well as changing the voltages at each lens electrodes. The objective of the design is often to get the electrons passing through the lens system to be focused at a specific point and to minimize the aberrations of the lens. To make such a design is a laborious task for electron optical lens designers. A fully-automated optimization routine to relieve the laborious manual design of charged particle lens systems has been demanded for years, however, to achieve this outcome while many free optimization parameters are involved in the lens system design is quite a challenge. This is mainly due to calculations of the lens electric potential which are in general carried out with very time-consuming techniques that require meshing of the lens space. Currently it is not difficult to find the electron-optical software to conduct accurate field calculations such as EOD, GPT, CPO, Simion, etc., that can be used in an optimization loop. However, it can take months to get the results. For instance, the design of even a simple system using COMSOL takes such a long computational time that the designer might not have the patience to wait for the result (assuming the computational memory allows them to do so). Therefore, although some charged particle optics design programs exist which could change a few geometrical parameters of the lenses or the voltages (EOD, GPT, CPO, Simion, etc.), a fully-automated optimization routine which could make a design where all geometries and voltages of the lenses could be varied in a feasible time did not exist. A first attempt (SOEM) was made more than 30 years ago, but it still had too many limitations to be widely used. The main objective of this thesis therefore was to find a technique which would enable electron optical designers to tackle this problem...
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Kruit, P., Supervisor
  • Hagen, C.W., Supervisor
  • Ghaffarian Niasar, M., Advisor
Award date24 Apr 2024
Print ISBNs978-94-6496-106-5
DOIs
Publication statusPublished - 2024

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