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Design Example: Underdamped Parallel RLC Circuit01:17

Design Example: Underdamped Parallel RLC Circuit

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Consider designing an oscillator circuit, a crucial component in various electronic devices and systems. The objective is to create an oscillator circuit with specific characteristics: a damped natural frequency of 4 kHz and a damping factor of 4 radians per second. To accomplish this, a parallel RLC circuit is employed, known for its ability to sustain oscillations at a resonant frequency. In this case, the damping factor is pivotal in achieving the desired performance.
Starting with a fixed...
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Design Example01:23

Design Example

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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

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In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
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Related Experiment Video

Updated: Aug 8, 2025

Fabrication of Silica Ultra High Quality Factor Microresonators
07:51

Fabrication of Silica Ultra High Quality Factor Microresonators

Published on: July 2, 2012

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Machine learning assisted inverse design of microresonators.

Arghadeep Pal, Alekhya Ghosh, Shuangyou Zhang

    Optics Express
    |March 2, 2023
    PubMed
    Summary

    Machine learning algorithms can predict microresonator geometry from dispersion profiles. Random Forest models, trained on simulation data, achieved below 15% average error, verified experimentally.

    Area of Science:

    • Photonics and Optical Engineering
    • Materials Science
    • Computational Physics

    Background:

    • Microresonators are crucial for optical applications, requiring precise geometry control for desired properties.
    • Dispersion significantly impacts optical nonlinearities and intracavity dynamics in microresonators.
    • Current methods for optimizing microresonator geometry are often complex and time-consuming.

    Purpose of the Study:

    • To develop and validate a machine learning (ML) approach for determining microresonator geometry from dispersion profiles.
    • To compare the performance of different ML algorithms for this inverse design problem.
    • To assess the accuracy and experimental feasibility of the ML-based method.

    Main Methods:

    • Generating a dataset of approximately 460 microresonator samples using finite element simulations.

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    Last Updated: Aug 8, 2025

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  • Training and evaluating two ML algorithms, including Random Forest, with hyperparameter tuning.
  • Experimentally verifying the best-performing ML model using integrated silicon nitride microresonators.

    • Main Results:

    • The Random Forest algorithm demonstrated superior performance in predicting microresonator geometry from dispersion data.
    • The ML model achieved an average error well below 15% on simulated data.
    • Experimental validation confirmed the model's effectiveness in real-world integrated photonic devices.

    Conclusions:

    • Machine learning offers an efficient and accurate tool for the inverse design of microresonators based on their optical dispersion.
    • The developed ML approach can accelerate the fabrication process of microresonators with tailored optical characteristics.
    • This study highlights the potential of ML in optimizing photonic device design and performance.