arXiv subjects

Hossein Esfandiar

Publications and source records attributed to Hossein Esfandiar.

5 recordsLinked to original sources

arXiv2026

Direct Wafer Bonding of Crystal-Ion-Sliced GaP Thin Films for Photonic Applications

Gallium phosphide (GaP) is a promising material platform for integrated photonics because of its high refractive index, broad optical transparency, and strong second-order nonlinear response. Here, we demonstrate GaP-on-insulator thin films fabricated by crystal ion slicing and direct wafer bonding, using fused silica and SiO$_2$/Si/Si thermally oxidized silicon substrates as representative platforms. Unlike GaP thin-film platforms that rely on heteroepitaxial growth or sacrificial-layer release, the presented approach enables the flexible integration of crystalline GaP thin films, independent of both donor and target substrates. Following post-transfer annealing, the films exhibit near-bulk crystalline quality with low residual strain, smooth surfaces suitable for nanophotonic fabrication, and homogeneous bonding interfaces. Furthermore, annealing restores the linear optical dispersion (n and k) approaching that of epitaxially grown GaP with estimated plane wave absorption loss of 0.9 dB/cm at 1550 nm in the telecom C-band. The demonstrated approach establishes a scalable pathway toward high-quality GaP thin-film photonics compatible with versatile heterogeneous integration and back-end-of-line CMOS processing.

physics.optics↗

arXiv2026

Stabilizing van der Waals NbOI2 by SiO2 encapsulation for Photonic Applications

Niobium oxide diiodide (NbOI2) is an emerging material for photonics and electronics, distinguished by its exceptional second-order nonlinearity and pronounced in-plane ferroelectricity, both originating from its highly anisotropic ABC-stacked crystal structure. Its broken inversion symmetry enables its optical nonlinear efficiency to scale with thickness, making multilayer NbOI2 highly promising for nonlinear frequency conversion like second harmonic generation or and spontaneous parametric down-conversion in bulk or waveguides. However, under ambient conditions NbOI2 degrades into an amorphous oxide within weeks, severely diminishing its nonlinear response. To overcome this, we investigate SiO2 encapsulation via physical vapor deposition to protect NbOI2 multilayers from environmental degradation. Our systematic study reveals that encapsulation preserves structural integrity and nonlinear optical performance, establishing NbOI2 as a stable candidate for heterogeneous integration in foundry-compatible photonic platforms and quantum technologies.

physics.optics↗

arXiv2025

Thickness-dependence of Linear and Nonlinear Optical Properties of Multilayer 3R-MoS2

3R-MoS2, a MoS2 polytype with broken inversion symmetry, enables unique light-matter interactions and is promising for linear and nonlinear integrated photonics beyond the monolayer limit. Yet, systematic studies of its thickness-dependent reflectivity and its impact on harmonic generation are still lacking. . Yet, systematic studies of its thickness-dependent reflectivity and its impact on harmonic generation are still lacking. Here, we introduce a non-destructive optical method to determine the thickness of 3r-MoS2 flakes from reflectivity measurements, offering AFM-like precision with a mean bias of less than 2 nm range, while being much faster and applicable to non-solid substrates such as PDMS, in the 3-200 nm range. Nonlinear characterization further reveals distinct thickness-dependent maxima in second- and third-harmonic generation (SHG/THG), with the first clear peak at ~200 nm. These maxima arise from Fabry-Pérot-type phase matching conditions mediated by the film thickness and can further be shaped by absorption. This work thus provides both a practical thickness metrology and new insights for exploiting thickness-dependent 3R-MoS2 nonlinearities in scalable photonic technologies.

physics.optics↗

arXiv2025

Structural and Optical Properties of Crystal Ion Sliced BaTiO$_3$ Thin Films

Barium titanate (BaTiO$_3$) is a compelling material for integrated photonics due to its strong electro-optic and second-order nonlinear properties. Crystal Ion Slicing (CIS) presents a scalable and CMOS-compatible route for fabricating thin BaTiO$_3$ films; however, ion implantation during CIS introduces lattice damage that can degrade structural and optical performance. In this study, we demonstrate that post-slicing thermal annealing effectively restores the structural integrity and optical quality of CIS-processed BaTiO$_3$ flakes. Raman spectroscopy confirms the recovery of crystallinity, while second-harmonic generation (SHG) microscopy reveals systematic reorientation of ferroelectric domains and restoration of the associated second-order nonlinear susceptibility tensor $X^{(2)}$. Notably, SHG signals persist even in regions with weak Raman signatures, indicating that long-range ferroelectric order can survive despite partial lattice disruption. Optical measurements show that the linear dispersion of annealed CIS flakes closely matches that of bulk BaTiO$_3$, validating their suitability for photonic integration. Together, these results qualify CIS - combined with thermal annealing - as a viable and scalable manufacturing strategy for high-quality BaTiO$_3$-on-insulator (BTOI) platforms, enabling advanced integrated photonic devices for modulation, frequency conversion, and quantum optics.

cond-mat.mtrl-sci↗

arXiv2023

YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach

Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed self-assembly (DSA) for which no traditional, automatic defect inspection software exists. Machine Learning-based SEM image analysis has become an increasingly popular research topic for defect inspection with supervised ML models often showing the best performance. However, little research has been done on obtaining a dataset with high-quality labels for these supervised models. In this work, we propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns while requiring minimal quality control effort from a DSA expert. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset which best reflects DSA expert defect labeling expectations. We discuss the strengths and limitations of our proposed labeling approach and suggest directions for future work in data-centric ML-based defect inspection.

cs.CV↗