Visible to the public Modeling Digital Low-Dropout Regulator with a Multiple Sampling Frequency Circuit Technology

TitleModeling Digital Low-Dropout Regulator with a Multiple Sampling Frequency Circuit Technology
Publication TypeConference Paper
Year of Publication2019
AuthorsGeng, J., Yu, B., Shen, C., Zhang, H., Liu, Z., Wan, P., Chen, Z.
Conference Name2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)
Date PublishedOct. 2019
PublisherIEEE
ISBN Number978-1-7281-2458-2
Keywordscircuit setup time, Circuit stability, digital low drop-out regulators, digital low dropout regulators, digital low-dropout regulator, digital low-dropout regulator modeling, high sampling frequency circuit, Integrated circuit modeling, low sampling frequency circuit output, low supply voltage, low-power electronics, Metrics, Model, multiple sampling frequencies, multiple sampling frequency circuit technology, pubcrawl, Regulators, resilience, Resiliency, sampling frequency circuit model, Scalability, security, setup time, short setup time, signal conditioning circuits, stabilization time, Time Frequency Analysis, Time-frequency Analysis, Voltage regulators
Abstract

The digital low dropout regulators are widely used because it can operate at low supply voltage. In the digital low drop-out regulators, the high sampling frequency circuit has a short setup time, but it will produce overshoot, and then the output can be stabilized; although the low sampling frequency circuit output can be directly stabilized, the setup time is too long. This paper proposes a two sampling frequency circuit model, which aims to include the high and low sampling frequencies in the same circuit. By controlling the sampling frequency of the circuit under different conditions, this allows the circuit to combine the advantages of the circuit operating at different sampling frequencies. This shortens the circuit setup time and the stabilization time at the same time.

URLhttps://ieeexplore.ieee.org/document/8925294/
DOI10.1109/ICASID.2019.8925294
Citation Keygeng_modeling_2019