The advancement of spintronics technology hinges on the efficient generation and control of spin-polarized currents, yet conventional approaches relying on magnetic materials are prone to external magnetic interference, limiting their practical applications. Andreev reflection spectroscopy has emerged as a powerful tool for probing material-specific properties such as spin polarization (P) and superconducting gaps (∆), but its theoretical foundations often rest on simplified models that assume isotropic interface scattering. This assumption neglects the ubiquitous spin-dependent anisotropic scattering observed in real-world interfaces, which can lead to significant misinterpretations of intrinsic material characteristics. To address this gap, our study aims to develop a comprehensive theoretical framework that incorporates anisotropic spin scattering effects, enabling a systematic investigation of how interface anisotropy modulates Andreev reflection spectra. This work seeks to resolve precision issues in the characterization of spin-polarized materials, particularly for emerging quantum systems like topological insulators, where accurate measurement of spin polarization is crucial but challenging.
Methodologically, we build upon the foundational Blonder-Tinkham-Klapwijk (BTK) model and its extension by Chen-Tesanovic-Chien (CTC) by introducing spin-dependent scattering parameters Z↑ and Z↓ to describe the distinct interface scattering strengths for spin-up and spin-down electrons. This allows us to construct a unified theoretical model applicable to a wide range of interface systems, from normal metals (P= 0) to half-metals (P= ±1). We employ detailed numerical calculations and three-dimensional image analysis to simulate the differential conductance spectra under varying conditions of spin polarization and interface anisotropy. Specifically, the model accounts for the probability amplitudes of Andreev reflection and normal reflection by solving the Bogoliubov-de Gennes equations with generalized boundary conditions, and the current formulae are derived by integrating over energy-dependent transmission probabilities, incorporating a judgment function to handle dominant spin channels realistically.
Our key results reveal several important physical insights. For non-magnetic metals (P=0), interface anisotropic scattering (e.g., Z↑ ≠ Z↓) can induce highly spin-polarized currents through a transmission spin polarization mechanism, as demonstrated by the suppression of Andreev reflection and the reduction in normalized differential conductance within the superconducting gap (e.g., decreasing from 2 to 0 as Z↑ increases while Z↓ is fixed). This effect is sensitive even to small values of Z (around 0.25–0.5), highlighting the importance of precise interface engineering. For magnetic materials with positive spin polarization (P＞0), such as those with P=0.25, anisotropy at the interface non-linearly modulates the current polarization; for instance, when Z↓ is fixed at 0.5, increasing initially enhances Andreev reflection due to balanced spin transmission but suppresses it beyond a critical point, illustrating the tunability of polarization rates. Conversely, for negatively polarized materials (P＜0), the spectra exhibit distinct features—such as the absence of peaks under certain conditions—enabling a novel method to determine the sign of P by comparing differential conductance behaviors. Experimental validation using pure Co films shows close agreement with our model, confirming its accuracy and the minor anisotropy in typical magnetic interfaces.
In conclusion, this theoretical framework not only refines the understanding of Andreev reflection spectroscopy by accounting for anisotropic scattering but also provides practical tools for characterizing quantum materials and designing spintronic devices. It offers new pathways for developing interference-resistant spin sources based on non-magnetic materials and optimizes interface engineering in magnetoresistance devices. Future work will focus on experimental extensions to low-dimensional systems and algorithmic improvements for parameter analysis, further bridging theory and application in quantum information science.