Design of electrostatic lenses through genetic algorithm and particle swarm optimisation methods integrated with...

Area of Science:

• Charged Particle Optics
• Computational Physics
• Optimization Techniques

Background:

• Differential algebra (DA) is effective for calculating aberration coefficients in electrostatic lenses.
• Genetic algorithms (GA) and particle swarm optimization (PSO) are powerful optimization tools.

Purpose of the Study:

• To optimize the geometry of an Einzel lens for improved performance in charged particle optics.
• To integrate GA and PSO with the DA method for lens optimization.
• To optimize a multi-component focusing column.

Main Methods:

• Generating initial random lens geometries.
• Applying GA and PSO algorithms to iteratively modify lens geometry.
• Utilizing DA to calculate third-order aberration coefficients and lens spot size for performance evaluation.

Main Results:

• Optimized Einzel lens designs were achieved through GA and PSO.
• The DA method accurately calculated aberrations and spot size, guiding the optimization.
• A focusing column consisting of two lenses and a Wien filter was successfully optimized using GA.

Conclusions:

• The integration of GA and PSO with DA provides an effective framework for optimizing charged particle optical systems.
• This approach enables the design of lenses with reduced aberrations and improved focusing capabilities.
• The optimized lens designs and focusing column show potential for enhanced performance in scientific instrumentation.