AI & ML
Experimental Characterization of SMD Inductors for Cryogenic Power Conversion at 4 K
arXiv cs.SY0 views
arXiv:2610.28923v1 Announce Type: new Abstract: With the increasing complexity and scalability demands of cryogenic electronic systems, such as quantum computing platforms, conversion of power within the cryogenic environment has become a primary concern. Magnetic inductors are among the key components affecting converter performance, as their electrical and magnetic properties can change significantly at low temperatures. In this paper, five commercially available Vishay SMD power inductors are experimentally characterized down to 4 K: DC resistance is measured continuously during cooldown, while frequency-dependent AC inductance and resistance are measured up to 2 MHz at both room temperature and 4 K. The results show substantial DC resistance reductions of 25x-58x at 4 K, while the inductance degrades by only about 5-5.5%. Although cryogenic operation slightly increases the AC resistance (due to the intensified skin effect) and raises core losses, converter-level analysis shows an inductor-only efficiency improvement of approximately 5.3-6.9 percentage points at 1 A, and up to 22.8 points at 3 A. The measured frequency-dependent impedance of each inductor is imported into Cadence and a complete single-phase 24 V-to-1 V buck converter with GaN power devices is simulated at 1, 2, and 3 A at 4 K. The full-converter efficiency at 4 K is shown to improve by 3.0-5.2 percentage points at 1 A and by up to 22.5 points at 3 A for the AC-characterized devices. These findings indicate that commercially available off-the-shelf power inductors remain operationally viable under cryogenic conditions and can simultaneously increase inductor efficiency and reduce the overall power loss in cryogenic power conversion systems.
Source:https://arxiv.org/abs/2610.28923PreviousFunnel-Precompensated Disturbance Observer for Uncertain Minimum-Phase SISO Linear Systems of Arbitrary Relative DegreeNextSAFTER: Safety-Aware Filtering Through Explicit Recoverability for Robust Reinforcement Learning under Distribution Shift
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