arXiv subjects

Junying Zhang

Publications and source records attributed to Junying Zhang.

10 recordsLinked to original sources

arXiv2026

Multiscale simulations guided advances for all-optical phase-change waveguides

Photonic computing using chalcogenide phase-change materials (PCMs) is under active development for energy-efficient artificial intelligence (AI) applications. A key requirement is to enable as many optically programmable levels per device as possible, while maintaining relatively low optical loss. In this work, we carry out multiscale simulations using density functional theory and finite-difference time-domain methods, proposing a "the shorter the better" strategy to optimize the performance of Sb2Te photonic waveguide devices. Our subsequent experimental characterizations of Sb2Te thin films and optical device measurements fully verify our theoretical predictions. In particular, we reveal the unconventional optical properties of metastable crystalline Sb2Te, and utilize these features for device design, yielding a simultaneous improvement in both the programming window and the optical loss. Overall, an optical programming precision exceeding 7-bit is achieved using a single waveguide cell, setting a new record for all-optical phase-change memory devices. Our work serves as a compelling example of computational material design, which demonstrates the predictive power of multiscale simulations in guiding the design of phase-change photonic devices for enhanced performance.

cond-mat.mtrl-sci↗

arXiv2026

Data-knowledge dual-driven intelligent framework for full-chain, experiment-efficient synthesis of 2D dendrites

Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic understanding of two-dimensional monatomic phase-change material for non-volatile optical applications

Elemental antimony (Sb) is a promising material for phase-change memory, neuromorphic computing and nanophotonic applications, because its compositional simplicity can prevent phase segregation upon extensive programming. Scaling down the film thickness is a necessary step to prolong the lifetime of amorphous Sb, but the optical properties of Sb are also significantly altered as the thickness is reduced to a few nanometers, adding complexity to device optimization. In this work, we aim to provide atomistic understanding of the thickness-dependent optical responses in Sb thin films. As thickness decreases, both the extinction coefficient and optical contrast reduce in the near-infrared spectrum, consistent with previous optical measurements. Such thickness dependence gives rise to a bottom thickness limit of 2 nm in photonic applications, as predicted by coarse-grained device simulations. Further bonding analysis reveals a fundamentally different behavior for amorphous and crystalline Sb upon downscaling, resulting in the reduction of optical contrast. Thin film experiments are also carried out to validate our predictions. The thickness-dependent optical trend is fully demonstrated by our ellipsometric spectroscopy experiments, and the bottom thickness limit of 2 nm is confirmed by structural characterization experiments. Finally, we show that the greatly improved amorphous-phase stability of the 2 nm Sb thin film enables robust and reversible optical switching in a silicon-based waveguide device.

cond-mat.mtrl-sci↗

arXiv2025

Effects of thermal annealing and film thickness on the structural and optical properties of indium-tin-oxide thin films

Indium-tin oxide (ITO) is a crucial functional layer for the optoelectronic applications, such as non-volatile color display thin films based on the ITO/phase-change material (PCM)/ITO/reflective metal multilayer structures on a silicon substrate. In addition to non-volatile color tuning by PCMs, thermally induced crystallization may alter the optical properties of ITO layers as well. But the potential change in color of the ITO layers is not considered so far. In this work, we investigate the structural and optical properties of ITO thin films via X-ray diffraction, spectroscopic ellipsometry and ultraviolet-visible spectrophotometry measurements. After thermal annealing at 250 {\deg}C, the ITO thin films of 15-100 nm get crystallized with strong changes in refractive index n and extinction coefficient k in the visible light range. However, for the 5-nm ITO thin film, crystallization is only observed after thermal annealing at 350 {\deg}C and the change in color is limited upon phase transition. We provide a colormap of the ITO/platinum/silicon structure in terms of the annealing temperature (150-350 {\deg}C) and ITO film thickness (5-100 nm). Our work suggests that the intrinsic change in colors of ITO layers should also be considered for the PCM-based reconfigurable display application.

physics.optics↗

arXiv2025

Amorphous phase-change memory alloy with no resistance drift

Spontaneous structural relaxation is intrinsic to glassy materials due to their metastable nature. For phase-change materials (PCMs), the resultant temporal change in electrical resistance seriously hamper in-memory computing (IMC) applications. Here, we report an ab-initio-calculation-informed design of amorphous PCM composed of robust "molecule-like" motifs with minimal Peierls distortion, depriving the amorphous alloy of structural ingredients that would gradually evolve upon aging to entail resistance drift. We demonstrate amorphous CrTe3 thin films that display practically no resistance drift at any working temperature from -200 to 165 degree C. We achieve multilevel programming of CrTe3 through both step-wise crystallization and step-wise amorphization using a hybrid opto-electronic device at various temperatures. Moreover, the application potential of CrTe3 in neuromorphic computing is testified by its incorporation in a vehicle with automatic path-tracking function. Our work opens a new avenue to achieving IMC-requisite properties via judicious design of the composition and atomic-level structure of disordered PCM alloys.

cond-mat.mtrl-sci↗

arXiv2025

Role of seed layer in growing atomically flat TiTe2/Sb2Te3 heterostructure thin films at the wafer scale

Chalcogenide phase-change materials (PCMs) are a leading candidate for advanced memory and computing applications. Epitaxial-like growth of chalcogenide thin films at the wafer scale is important to guarantee the homogeneity of the thin film but is challenging with magnetron sputtering, particularly for the growth of phase-change heterostructure (PCH), such as TiTe2/Sb2Te3. In this work, we report how to obtain highly textured TiTe2/Sb2Te3 heterostructure thin films with atomically sharp interfaces on standard silicon substrates. By combining atomic-scale characterization and ab initio simulations, we reveal the critical role of the Sb2Te3 seed layer in forming a continuous Si-Sb-Te mixed transition layer, which provides a wafer-scale flat surface for the subsequent epitaxial-like growth of TiTe2/Sb2Te3 thin film. By gradually reducing the thickness of the seed layer, we determine its critical limit to be ~2 nm. Non-negligible in-plane tensile strain was observed in the TiTe2 slabs due to the lattice mismatch with the adjacent Sb2Te3 ones, suggesting that the chemical interaction across the structural gaps in the heterostructure is stronger than a pure van der Waals interaction. Finally, we outline the potential choices of chalcogenides for atomically flat seed layers on standard silicon substrates, which can be used for wafer-scale synthesis of other high-quality PCM or PCH thin films.

cond-mat.mtrl-sci↗

arXiv2024

BeniFul: Backdoor Defense via Middle Feature Analysis for Deep Neural Networks

Backdoor defenses have recently become important in resisting backdoor attacks in deep neural networks (DNNs), where attackers implant backdoors into the DNN model by injecting backdoor samples into the training dataset. Although there are many defense methods to achieve backdoor detection for DNN inputs and backdoor elimination for DNN models, they still have not presented a clear explanation of the relationship between these two missions. In this paper, we use the features from the middle layer of the DNN model to analyze the difference between backdoor and benign samples and propose Backdoor Consistency, which indicates that at least one backdoor exists in the DNN model if the backdoor trigger is detected exactly on input. By analyzing the middle features, we design an effective and comprehensive backdoor defense method named BeniFul, which consists of two parts: a gray-box backdoor input detection and a white-box backdoor elimination. Specifically, we use the reconstruction distance from the Variational Auto-Encoder and model inference results to implement backdoor input detection and a feature distance loss to achieve backdoor elimination. Experimental results on CIFAR-10 and Tiny ImageNet against five state-of-the-art attacks demonstrate that our BeniFul exhibits a great defense capability in backdoor input detection and backdoor elimination.

cs.CR↗

arXiv2020

Identification and Validation of the SNV Biomarkers Based on Multi-Dimensional Patterns

Background: Single nucleotide variants (SNVs) are detected as different distributions of DNA samples of distinct types of cancer patients. Even though, it is an exacting task to select the appropriate method to identify cancer to the greatest extent of SNVs. Results: In this paper, we proposed a biomarker concept based on SNV patterns in different feature dimensions. Raw dataset (2761 samples) consisting of twelve different cancers was obtained from TCGA (The Cancer Genome Atlas). After preliminary screening of 562,321 DNA mutation sites in the samples, the mutation sites were extracted and characterized by cancer types in six different SNV feature dimensions. In this study, we found that the extracted features showed similar distribution in the cluster center of the disease type of the samples. After the initial processing of the raw data, the sample was more focused on the subtype distribution of the cancer or the cancer at the SNV level. We used k-nearest neighbors (KNN) to classify the extracted features and Leave-One-Out cross verified them. The accuracy of classifying is stable at around 97% and reached 97.43% at the highest. During the validation phase, we found validated oncogenes in the loci of the features with the highest importance among nine cancers. Conclusions: In summary, the samples showed consistent patterns according to the cancer in which it belongs. It is feasible to classify the cancer of the sample by the distribution of different dimensions of the SNVs and has a high accuracy. And has potential implications for the discovery of cancer-causing genes.

q-bio.QM↗

arXiv2019

A new Gini correlation between quantitative and qualitative variables

We propose a new Gini correlation to measure dependence between a categorical and numerical variables. Analogous to Pearson $R^2$ in ANOVA model, the Gini correlation is interpreted as the ratio of the between-group variation and the total variation, but it characterizes independence (zero Gini correlation mutually implies independence). Closely related to the distance correlation, the Gini correlation is of simple formulation by considering the nature of categorical variable. As a result, the proposed Gini correlation has a lower computational cost than the distance correlation and is more straightforward to perform inference. Simulation and real applications are conducted to demonstrate the advantages.

stat.ME↗

arXiv2008

Adaptive Affinity Propagation Clustering

Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter 'preference' can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clustering solution, adaptive adjustment of damping factors to eliminate oscillations, and adaptive escaping from oscillations when the damping adjustment technique fails. Experimental results on simulated and real data sets show that the adaptive AP is effective and can outperform AP in quality of clustering results.

cs.AI↗