Frequency-Phase-Locking Mechanism inside DC SQUIDs and The Analytical Expression of Current-Voltage Characteristics

TL;DR

This paper introduces a frequency-phase-locking model and analytical expression for dc SQUIDs, providing insights into their current-voltage characteristics and enabling simplified practical analysis of their performance.

Contribution

The work develops a novel FPL model and analytical expression that explain the internal dynamics and current-voltage behavior of dc SQUIDs, which were previously lacking general models.

Findings

The FPL model reveals dc SQUIDs as FPL systems internally.

The current-voltage characteristics are projections of three network impedances.

The analytical expression accurately predicts the I-V characteristics.

Abstract

Direct-current superconducting quantum interference devices (dc SQUIDs) are ultra-sensitive flux-to-voltage convertors widely applied for biomagnetism and geophysics; they are working as the magnetic-field-effect transistors (MFETs) for their flux-modulated current-voltage characteristics. However, unlike semiconductor FETs, dc SQUIDs lack general analytical models and expressions to interpret the inner dynamics and outside current-voltage characteristics. This work presents a frequency-phase-locking (FPL) model and the analytical expression to reveal the how the current-voltage characteristics are formed inside dc SQUIDs, and how the characteristics are quantitively decided by the circuit parameters. The application of the analytical expression for the calculations of current-voltage characteristics is demonstrated; the results are compared with the numerical simulations. It is shown that a dc-SQUID is an FPL system inside and a current-modified nonlinear resistor outside; its current-voltage characteristics formed by FPL mechanism are the projections of three network impedances driven by Josephson currents. Based on the FPL model and the analytical expression, dc SQUIDs can be simply treated as nonlinear resistors in practical applications; their performance can be directly predicted and evaluated according to three network impedances.