PiezoNet and Data-Driven Models for Time-Domain Characterization of Piezoelectric Resonators

Davit Grigoryan, Mian Liao, Haoran Li, Shukai Wang, Tanuj Sen, Matthew Tan, Minjie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a fully automated data acquisition platform and the resulting database - PiezoNet1 - for data-driven time-domain characterization of piezoelectric resonators used in power electronics. The platform measures the voltage and the current over piezoelectric resonators across a wide range of excitation waveforms and ambient temperatures. The power stage dynamically adjusts to efficient operating points for best operation under different load conditions. This system is mechanically versatile, accommodating crystals of diverse materials and dimensions. The platform enables comprehensive and precise characterization by automating the data collection process, thereby providing extensive datasets essential for training data-driven models (e.g., neural networks) to predict operating points and nonlinear behaviors, and to quantify the sample-to-sample variation of piezoelectric resonators. A family of sequence-to-sequence neural network models were trained and tested to validate the feasibility of time-domain data-driven models for piezoelectric resonators.

Original languageEnglish (US)
Title of host publicationAPEC 2025 - 14th Annual IEEE Applied Power Electronics Conference and Exposition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1882-1888
Number of pages7
ISBN (Electronic)9798331516116
DOIs
StatePublished - 2025
Event14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025 - Atlanta, United States
Duration: Mar 16 2025Mar 20 2025

Publication series

NameConference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC
ISSN (Print)1048-2334
ISSN (Electronic)2470-6647

Conference

Conference14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025
Country/TerritoryUnited States
CityAtlanta
Period3/16/253/20/25

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • data acquisition
  • data-driven methods
  • hysteresis loop
  • machine learning
  • piezoelectric resonator

Fingerprint

Dive into the research topics of 'PiezoNet and Data-Driven Models for Time-Domain Characterization of Piezoelectric Resonators'. Together they form a unique fingerprint.

Cite this