Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.38404

Energy-Efficient Non-Volatile Photonic Switching via Composition-Engineered Sn-Doped GST

Xingyu Zhang·Daniel Vaz·Chi-Yi Kao·Marcus Tamura·Xiong Feng·Carlos A. Rios Ocampo·Bhavin J. Shastri·Nathan Youngblood
View original source ↗Read source document ↗Export metadata ↓

Abstract

Integrated phase-change photonics allows non-volatile optical switching with no static power draw, but the electrical energy needed to reversibly switch the material's state limits practical scaling. Efforts to improve energy efficiency have mostly focused on device optimization, such as redesigning the integrated microheater or waveguide. Here, we take a different, materials-centered approach by alloying Sn to Ge$_2$Sb$_2$Te$_5$ (GST) without altering the device structure or CMOS compatibility. We explore Sn concentrations ranging from 0 at.% to 20 at.% on waveguide-integrated PN and PIN heaters and observe a reduction in both the amorphization and crystallization energies in the 5--10 at.% range. At higher Sn content this trend reverses and the switching energy increases, which is suggestive of phase segregation when combined with TEM compositional analysis. A 10 at.% Sn device continues to switch over 1000 cycles, with observable degradation in the extinction ratio from 4.5 $\pm$ 0.3 dB to 2.4 $\pm$ 0.3 dB. Post-cycling imaging shows PCM migration and void formation which we believe can be further improved by enhanced encapsulation. Overall, Sn alloying offers a route to lower programming energy in integrated phase-change photonics without negatively affecting the switching speed relative to un-doped GST.

Explore related subjects

cond-mat.mtrl-sciphysics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xingyu Zhang, Daniel Vaz, Chi-Yi Kao, Marcus Tamura, Xiong Feng, Carlos A. Rios Ocampo, Bhavin J. Shastri, Nathan Youngblood. 2026-09-29. Energy-Efficient Non-Volatile Photonic Switching via Composition-Engineered Sn-Doped GST. https://arxiv.org/abs/2609.38404

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality

Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as it is not known a priori which portions of the data are essential for accurate learning. Here, we reveal significant redundancies in electronic structure datasets across diverse material systems and attribute them to the low intrinsic dimensionality of the underlying data. We show that even random pruning can substantially reduce dataset size with minimal degradation in predictive accuracy. Moreover, a state-of-the-art coverage-based pruning strategy that samples data across all learning difficulties almost always preserves chemical accuracy and maintains model generalizability while using up to two orders of magnitude less data and reducing training time by a factor of three or more. We further demonstrate that the essential electronic structure information lies on a low-dimensional, non-linear manifold, providing a potential geometric explanation for the observed prunability. These observations are consistent with the predominance of local atomic environments in determining electronic properties, as suggested by nearsightedness arguments, and indicate that large-scale datasets may contain highly overlapping information. Our findings challenge the prevailing assumption that such extensive datasets are necessary for accurate ML-based electronic structure predictions and open a path toward identifying minimal, representative datasets for each material class.

cond-mat.mtrl-sci↗
arXiv2026

Large-gap quantum anomalous Hall insulator in two-dimensional pentagonal TiN$_8$ monolayer

Discovering the quantum anomalous Hall (QAH) effect in two-dimensional magnets is a central goal of topological condensed matter physics, yet candidate materials combining a sizable gap with a well-defined Chern number remain scarce. We report a new material platform that realizes emergent magnetism and nontrivial band topology within the largely overlooked family of two-dimensional MN$_8$ monolayers. Through first-principles calculations, we reveal that these systems exhibit out-of-plane magnetic ground states and nontrivial topological features driven by the localized $d$-orbitals of the embedded transition metals. Remarkably, we identify TiN$_8$ as a quantum anomalous Hall insulator characterized by a Chern number of $C=-1$ with a large topological gap of 223 meV. As a companion system, MoN$_8$ is predicted to be a rare high-Chern-number QAH insulator, with a Chern number of $C=2$, although its much smaller gap of 12 meV renders it more fragile. These findings establish the penta-MN$_8$ family as a versatile and promising platform to realize exotic topological quantum states. This work not only broadens the material landscape for magnetic topological insulators but also lays a solid theoretical foundation for the development of next-generation spintronic and quantum computing devices.

cond-mat.mtrl-sci↗
arXiv2026

Prediction of Magnetic Topological Materials Combining Spin and Magnetic Space Groups

Recent developments in spin space groups (SSGs) have led to a new classification of magnetic order and, with it, the emergence of altermagnetism. However, an efficient and routine approach that predicts electronic band topology in magnetic materials by SSGs, and especially the evolution of band topology from negligible spinorbit coupling (SOC) to finite SOC with the symmetry lowering from SSGs to magnetic space groups (MSGs) determined by magnetic-moment directions, has not yet been established. Here, we propose a scheme combining SSG and its MSG subgroups to diagnose band topology in collinear magnets using symmetry indicators of electronic band topology, established for all the 1,421 SSGs and 3,420 MSG subgroups. Specifically, the compatibility relations from SSG to the MSG subgroups can enable a complete topological classification for all possible magnetic-moment directions from the first-principles calculated numbers of occurrences of irreducible (co-)representations at high-symmetry points without SOC with respect to SSG. This scheme can be directly applied to collinear magnets whose magnetic structures have already been determined experimentally or theoretically. Applying it to 488 collinear magnets from MAGNDATA with experimentally determined magnetic structures, we identify 26 materials that are trivial by MSG but are expected to inherit topology protected by SSG symmetry. We showcase Dirac nodal lines and boundary states predicted by SSG but invisible to MSG in FePSe$_3$. Our work demonstrates the predictive power of the approach combining SSG and MSG over the approaches based on either group alone. The results of high-throughput calculations are expected to extend the magnetic topological materials pool, facilitating future experimental realizations of more magnetic topological materials.

cond-mat.mtrl-sci↗