arXiv subjects

Zifei Zhang

Publications and source records attributed to Zifei Zhang.

4 recordsLinked to original sources

arXiv2026

Scalable Cyclic Olefin Copolymer Encapsulation for High Optical Quality of TMD Monolayers

Monolayer transition metal dichalcogenides (TMDs) combine a direct bandgap, strongly bound excitons, and pronounced second-order optical nonlinearity, which makes them promising materials for ultrathin optoelectronic and nanophotonic devices. However, their optical performance is often degraded by environmental exposure and substrate-induced charge trapping, motivating the development of scalable encapsulation strategies. Here, we investigate spin-coated cyclic olefin copolymer (COC) as a scalable encapsulant for TMDs. Room-temperature and cryogenic optical spectroscopy reveal enhanced photoluminescence and second-harmonic generation, accompanied by excitonic linewidth narrowing and an increased exciton-to-trion ratio. In addition, COC encapsulation induces an excitonic peak splitting and an overall spectral blueshift. First-principles calculations attribute these spectral modifications to local symmetry breaking at the chalcogen interface and macroscopic compressive strain, respectively. These findings establish spin-coated COC as an effective, scalable encapsulation strategy and a potential platform for post-growth excitonic and band-structure engineering.

cond-mat.mtrl-sci↗

arXiv2026

Gallium phosphide on insulator for nanophotonics and quantum technologies

Gallium phosphide is a promising material platform for visible and near-infrared photonics and quantum technologies owing to its high refractive index, low optical absorption, and strong second-order nonlinearity. Here, we demonstrate the fabrication of GaP-on-insulator substrates by ion slicing. The splitting depth and exfoliation behavior of bulk GaP are tailored by controlling the He$^{+}$ ion implantation energy and fluence, enabling thin-film transfer onto amorphous substrates by anodic bonding and plasma-enhanced direct wafer bonding. Channeling Rutherford backscattering spectrometry and X-ray diffraction confirm that the transferred layers retain their single-crystalline structure, while implantation-induced disorder and optical absorption are substantially reduced by annealing at 500 °C and subsequent polishing. The annealed films exhibit linear optical properties approaching those of bulk GaP. In addition, a (110)-oriented GaP thin film shows the characteristic polarization dependence expected from the zinc-blende second-order nonlinear susceptibility tensor, demonstrating a near-pristine second-order nonlinear response. This flexible fabrication approach enables the integration of high-quality single-crystalline GaP with variable orientation for free-space and integrated nanophotonics as well as nonlinear and quantum optical devices.

physics.optics↗

arXiv2020

Defense-guided Transferable Adversarial Attacks

Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbations on clean inputs. Under the query-free black-box scenario, adversarial examples are hard to transfer to unknown models, and several methods have been proposed with the low transferability. To settle such issue, we design a max-min framework inspired by input transformations, which are benificial to both the adversarial attack and defense. Explicitly, we decrease loss values with inputs' affline transformations as a defense in the minimum procedure, and then increase loss values with the momentum iterative algorithm as an attack in the maximum procedure. To further promote transferability, we determine transformed values with the max-min theory. Extensive experiments on Imagenet demonstrate that our defense-guided transferable attacks achieve impressive increase on transferability. Experimentally, we show that our ASR of adversarial attack reaches to 58.38% on average, which outperforms the state-of-the-art method by 12.1% on the normally trained models and by 11.13% on the adversarially trained models. Additionally, we provide elucidative insights on the improvement of transferability, and our method is expected to be a benchmark for assessing the robustness of deep models.

cs.LG↗

arXiv2020

AdvJND: Generating Adversarial Examples with Just Noticeable Difference

Compared with traditional machine learning models, deep neural networks perform better, especially in image classification tasks. However, they are vulnerable to adversarial examples. Adding small perturbations on examples causes a good-performance model to misclassify the crafted examples, without category differences in the human eyes, and fools deep models successfully. There are two requirements for generating adversarial examples: the attack success rate and image fidelity metrics. Generally, perturbations are increased to ensure the adversarial examples' high attack success rate; however, the adversarial examples obtained have poor concealment. To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference coefficients, in the constraint of a distortion function when generating adversarial examples. In fact, the visual subjective feeling of the human eyes is added as a priori information, which decides the distribution of perturbations, to improve the image quality of adversarial examples. We tested our method on the FashionMNIST, CIFAR10, and MiniImageNet datasets. Adversarial examples generated by our AdvJND algorithm yield gradient distributions that are similar to those of the original inputs. Hence, the crafted noise can be hidden in the original inputs, thus improving the attack concealment significantly.

cs.CV↗