Artificial Neural Networks (ANNs) Modeling of Acoustic Wave Resonators and Filters

Description:

This thesis explores the application of artificial neural networks (ANNs) as surrogate models for simulating and designing high-Q piezoelectric acoustic wave resonators and filters. Traditional modeling techniques such as finite element methods or analytical approaches can be computationally intensive or limited in scope. ANNs offer a data-driven alternative that can learn complex electroacoustic behaviors from simulation or experimental data, enabling fast and accurate predictions. The work aims to develop and validate ANN-based models that can support RF and microwave design tasks, particularly in the context of high-performance acoustic components.

Objectives of the Work:

The goal of this thesis is to investigate and demonstrate the effectiveness of artificial neural networks in modeling the behavior of acoustic wave resonators and filters. Depending on personal interests, prior knowledge, and the ECTS requirements of the degree program, the task will be tailored to the individual. 

Research Questions (Possible questions include):

How accurately can ANNs model the electroacoustic behavior of high-Q piezoelectric resonators and filters?

What are the advantages and limitations of ANN-based modeling compared to traditional numerical or analytical methods?

How can ANN models be integrated into RF/microwave design processes?

What types of neural network architectures are most suitable for modeling acoustic wave devices?

How does the quality and quantity of training data affect model performance?

Topics:

The work may include the following components such as:

Developing a dataset from simulations or measurements of acoustic wave devices.

Designing and training neural network architectures to emulate device behavior.

Validating model accuracy and generalization capability.

Comparing ANN performance with conventional modeling techniques.

Exploring integration of ANN models into design workflows for RF/microwave systems.

Skills (What to Bring):

Basic knowledge of electronics and circuit development

Interest in high-frequency technology, medical technology and analog circuit design

Willingness to familiarize yourself with modern tools such as ADS

Advantageous (but not essential):

Initial experience in RF design.

Thanks and Regards,

Vikrant Chauhan (vikrant.chauhan@fau.de)