An Ultra-Low Quiescent Current Adaptive ZCD Technique for Discontinuous-Conduction-Mode Control Applications
A low-power, adaptive zero-current-detection (AZCD) technique for DC-DC converter operating in discontinuous conduction mode (DCM) is proposed in this paper. In this design, current-input comparator is utilized for zero current switch (ZCS) to achieve and low-power performances. Besides, adaptive offset voltage technique (AOVT) is proposed to dynamically adjust comparator’s offset voltage for the on-time calibration of freewheeling transistor. What’s more, an improved latch-comparator is employed to accurately detect the turn-off timing of freewheeling transistor and generate corresponding signal to automatically tune the offset voltage at a desired point automatically. Thanks to the hybrid design of the latch comparator and analog comparator, the system not only achieves zero static power consumption outside the testing window but also reduces the power consumption requirements for the analog comparator, thereby meeting the low-power demands of internet of things (IoT) applications. The proposed AZCD circuit has been fabricated with a 0.35 m BCD process and occupied less than 0.0131 mm2 area. The quiescent power consumption of the entire AZCD module is less than 3 nA. Compared to the control group without adaptive functionality, the efficiency is improved from 80.77% to 84.68% at 1 mA.
Authors
- Lichen Peng
- Yue Shi (ORCID: https://orcid.org/0000-0002-0033-0478)
- Shidong Wang
- Bo Zhang (ORCID: https://orcid.org/0000-0003-1288-1549)
- Xudong Li (ORCID: https://orcid.org/0000-0002-3389-8369)
- Zekun Zhou (ORCID: https://orcid.org/0000-0002-8726-2657)
- Xinyang Liu (ORCID: https://orcid.org/0009-0009-7592-4316)
Institutions
- University of Electronic Science and Technology of China (CN)
- National Engineering Research Center of Electromagnetic Radiation Control Materials (CN)
Publication Details
- Journal
- IEEE Transactions on Circuits & Systems II Express Briefs
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1109/tcsii.2025.3571713
- Citations
- 2
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Sichuan Province