arXiv subjects

Haozhe Wang

Publications and source records attributed to Haozhe Wang.

108 records · Page 6Linked to original sources

arXiv2023

Isotropic plasma-thermal atomic layer etching of superconducting TiN films using sequential exposures of molecular oxygen and SF$_6/$H$_2$ plasma

Microwave loss in superconducting titanium nitride (TiN) films is attributed to two-level systems in various interfaces arising in part from oxidation and microfabrication-induced damage. Atomic layer etching (ALE) is an emerging subtractive fabrication method which is capable of etching with Angstrom-scale etch depth control and potentially less damage. However, while ALE processes for TiN have been reported, they either employ HF vapor, incurring practical complications; or the etch rate lacks the desired control. Further, the superconducting characteristics of the etched films have not been characterized. Here, we report an isotropic plasma-thermal TiN ALE process consisting of sequential exposures to molecular oxygen and an SF$_6$/H$_2$ plasma. For certain ratios of SF$_6$:H$_2$ flow rates, we observe selective etching of TiO$_2$ over TiN, enabling self-limiting etching within a cycle. Etch rates were measured to vary from 1.1 Å/cycle at 150 $^\circ$C to 3.2 Å/cycle at 350 $^\circ$C using ex-situ ellipsometry. We demonstrate that the superconducting critical temperature of the etched film does not decrease beyond that expected from the decrease in film thickness, highlighting the low-damage nature of the process. These findings have relevance for applications of TiN in microwave kinetic inductance detectors and superconducting qubits.

cond-mat.mes-hall↗

arXiv2023

Erbium-excess gallium garnets

A series of garnets of formula Er3+xGa5-xO12 is described, for which we report the crystal structures for both polycrystalline and single-crystal samples. The x limit in the garnet phase is between 0.5 and 0.6 under our conditions, with the Er fully occupying the normal garnet site plus half-occupying the octahedral site at x = 0.5 in place of the Ga normally present. Long-range antiferromagnetic order with spin ice-like frustration is suggested by the transition temperature (TN=0.8K) being much lower than the Curie-Weiss theta. The magnetic ordering temperature does not depend on the Er excess, but there is increasing residual entropy as the Er excess is increased, highlighting the potential for unusual magnetic behavior in this system.

cond-mat.mtrl-sci↗

arXiv2023

Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning

The proliferation of the Internet has led to the emergence of online advertising, driven by the mechanics of online auctions. In these repeated auctions, software agents participate on behalf of aggregated advertisers to optimize for their long-term utility. To fulfill the diverse demands, bidding strategies are employed to optimize advertising objectives subject to different spending constraints. Existing approaches on constrained bidding typically rely on i.i.d. train and test conditions, which contradicts the adversarial nature of online ad markets where different parties possess potentially conflicting objectives. In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors. Instead of relying on the i.i.d. assumption, our insight is to align the train distribution of environments with the potential test distribution meanwhile minimizing policy regret. Based on this insight, we propose a practical Minimax Regret Optimization (MiRO) approach that interleaves between a teacher finding adversarial environments for tutoring and a learner meta-learning its policy over the given distribution of environments. In addition, we pioneer to incorporate expert demonstrations for learning bidding strategies. Through a causality-aware policy design, we improve upon MiRO by distilling knowledge from the experts. Extensive experiments on both industrial data and synthetic data show that our method, MiRO with Causality-aware reinforcement Learning (MiROCL), outperforms prior methods by over 30%.

cs.LG↗

arXiv2023

Synthesis and physical properties of a new layered ferromagnet, Cr1.21Te2

Single crystals of a new layered compound, Cr1.21Te2, was synthesized via a vapor transport method. The crystal structure and physical properties were characterized by single crystal and powder x-ray diffraction, temperature- and field-dependent magnetization, zero-field heat capacity and angle-resolved photoemission spectroscopy. Cr1.21Te2, containing two Cr sites, crystalizes in a trigonal structure with a space group P-3 (No. 147). The Cr site in the interstitial layer is partially occupied. Physical property characterizations indicate that Cr1.21Te2 is metallic with hole pockets at the Fermi energy and undergoes a ferromagnetic phase transition at ~173 K. The magnetic moments align along the c-axis in the ferromagnetic state. Based on low temperature magnetization, the spin stiffness constant D and spin excitation gap $Δ$ were estimated according to Bloch's law to be D = 99 $\pm$ 24 meV $Å^2$ and $Δ$ = 0.46 $\pm$ 0.33 meV, suggesting its possible application as a low dimensional ferromagnet.

cond-mat.mtrl-sci↗

arXiv2023

Non-centrosymmetric Sr$_{2}$IrO$_{4}$ obtained under High Pressure

Sr$_{2}$IrO$_{4}$ with strong spin-orbit coupling (SOC) and Hubbard repulsion (U) hosts Mott insulating states. The similar crystal structure, magnetic and electronic properties, particularly the $d$-wave gap observed in Sr$_{2}$IrO$_{4}$ enhanced the analogies to cuprate high-$T_{c}$ superconductor, La$_{2}$CuO$_{4}$. The incomplete analogy was due to the lack of broken inversion symmetry phases observed in Sr$_{2}$IrO$_{4}$. Here, under high pressure and high temperature conditions, we report a non-centrosymmetric Sr$_{2}$IrO$_{4}$. The crystal structure and its noncentrosymmetric character were determined by single crystal X-ray diffraction and high-resolution scanning transmission electron microscopy (HR-STEM). The magnetic characterization confirms the Ir$^{4+}$ with $S$ = 1/2 at low temperature in Sr$_{2}$IrO$_{4}$ with magnetic ordering occurred at around 86 K, where a larger moment is observed than the ambient pressure Sr$_{2}$IrO$_{4}$. Moreover, the resistivity measurement shows three-dimensional Mott variable-range hopping existed in the system. This non-centrosymmetric Sr$_{2}$IrO$_{4}$ phase appears to be a unique material to offer further understanding of high-$T_{c}$ superconductivity.

cond-mat.str-el↗

arXiv2023

Synergistic Photon Management and Strain-Induced Band Gap Engineering of Two-Dimensional MoS2 Using Semimetal Composite Nanostructures

2D MoS2 attracts increasing attention for its application in flexible electronics and photonic devices. For 2D material optoelectronic devices, light absorption of the molecularly thin 2D absorber would be one of the key limiting factors in device efficiency, and conventional photon management techniques are not necessarily compatible with them. In this paper, we show two semimetal composite nanostructures for synergistic photon management and strain-induced band gap engineering of 2D MoS2: (1) pseudo-periodic Sn nanodots, (2) conductive SnOx (x<1) core-shell nanoneedle structures. Without sophisticated nanolithography, both nanostructures are self-assembled from physical vapor deposition. 2D MoS2 achieves up to >15x enhancement in absorption at λ=650-950 nm under Sn nanodots, and 20-30x at λ=700-900 nm under SnOx (x<1) nanoneedles, both spanning from visible to near infrared regime. Enhanced absorption in MoS2 results from strong near field enhancement and reduced MoS2 band gap due to the tensile strain induced by the Sn nanostructures, as confirmed by Raman and photoluminescence spectroscopy. Especially, we demonstrate that up to 3.5% biaxial tensile strain is introduced to 2D MoS2 using conductive nanoneedle-structured SnOx (x<1), which reduces the band gap by ~0.35 eV to further enhance light absorption at longer wavelengths. To the best of our knowledge, this is the first demonstration of a synergistic triple-functional photon management, stressor, and conductive electrode layer on 2D MoS2. Such synergistic photon management and band gap engineering approach for extended spectral response can be further applied to other 2D materials for future 2D photonic devices.

cond-mat.mes-hall↗

arXiv2022

Isotropic plasma-thermal atomic layer etching of aluminum nitride using SF$_6$ plasma and Al(CH$_3$)$_3$

We report the isotropic plasma atomic layer etching (ALE) of aluminum nitride using sequential exposures of SF$_6$ plasma and trimethylaluminum (Al(CH$_3$)$_3$, TMA). ALE was observed at temperatures greater than 200 $^\circ$C, with a maximum etch rate of 1.9 Å/cycle observed at 300 $^\circ$C as measured using ex-situ ellipsometry. After ALE, the etched surface was found to contain a lower concentration of oxygen compared to the original surface and exhibited a $\sim 35$% decrease in surface roughness. These findings have relevance for applications of AlN in nonlinear photonics and wide bandgap semiconductor devices.

cond-mat.mes-hall↗

arXiv2022

ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially the return-on-investment (ROI) constraint. ROIs change non-monotonically during the sequential bidding process, and often induce a see-saw effect between constraint satisfaction and objective optimization. While some existing approaches show promising results in static or mildly changing ad markets, they fail to generalize to highly dynamic ad markets with ROI constraints, due to their inability to adaptively balance constraints and objectives amidst non-stationarity and partial observability. In this work, we specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, our method exploits an indicator-augmented reward function free of extra trade-off parameters and develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework to adaptively control the constraint-objective trade-off in non-stationary ad markets. Extensive experiments on a large-scale industrial dataset with two problem settings reveal that CBRL generalizes well in both in-distribution and out-of-distribution data regimes, and enjoys superior learning efficiency and stability.

cs.LG↗

arXiv2022

LiYbSe2: Frustrated Magnetism in a New Pyrochlore Lattice

Three-dimensionally (3D) frustrated magnets generally exist in the magnetic diamond and pyrochlore lattices, in which quantum fluctuations suppress magnetic orders and generate highly entangled ground states (GS). LiYbSe2 in a previously unreported pyrochlore lattice was discovered from LiCl flux growth. Distinct from the quantum spin liquid (QSL) candidate NaYbSe2 hosting a perfect triangular lattice of Yb3+, LiYbSe2 crystallizes in the cubic pyrochlore structure with space group Fd-3m (No. 227). The Yb3+ ions in LiYbSe2 are arranged on a network of corner-sharing tetrahedra, which is particularly susceptible to geometrical frustration. According to our temperature-dependent magnetic susceptibility measurements, the dominant antiferromagnetic interaction in LiYbSe2 is expected to appear around 8 K. However, no long-range magnetic order is detected in thermomagnetic measurements above 70 mK. Specific heat measurements also show magnetic correlations shifting with applied magnetic field with a degree of missing entropy that may be related to the slight mixture of Yb3+ on the Li site. Such magnetic frustration of Yb3+ is rare in pyrochlore structures. Thus, LiYbSe2 shows promises in intrinsically realizing disordered quantum states like QSL in pyrochlore structures.

cond-mat.str-el↗

arXiv2022

Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning

Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks. While previous efforts attempt to address this challenge using meta-learning strategies, they typically suffer from sampling inefficiency with on-policy RL algorithms or meta-overfitting with off-policy learning. In this work, we propose a novel meta-RL strategy to address those limitations. In particular, we decompose the meta-RL problem into three sub-tasks, task-exploration, task-inference and task-fulfillment, instantiated with two deep network agents and a task encoder. During meta-training, our method learns a task-conditioned actor network for task-fulfillment, an explorer network with a self-supervised reward shaping that encourages task-informative experiences in task-exploration, and a context-aware graph-based task encoder for task inference. We validate our approach with extensive experiments on several public benchmarks and the results show that our algorithm effectively performs exploration for task inference, improves sample efficiency during both training and testing, and mitigates the meta-overfitting problem.

cs.LG↗

arXiv2022

Multiplication of freestanding semiconductor membranes from a single wafer by advanced remote epitaxy

Freestanding single-crystalline membranes are an important building block for functional electronics. Especially, compounds semiconductor membranes such as III-N and III-V offer great opportunities for optoelectronics, high-power electronics, and high-speed computing. Despite huge efforts to produce such membranes by detaching epitaxial layers from donor wafers, however, it is still challenging to harvest epitaxial layers using practical processes. Here, we demonstrate a method to grow and harvest multiple epitaxial membranes with extremely high throughput at the wafer scale. For this, 2D materials are directly formed on III-N and III-V substrates in epitaxy systems, which enables an advanced remote epitaxy scheme comprised of multiple alternating layers of 2D materials and epitaxial layers that can be formed by a single epitaxy run. Each epilayer in the multi-stack structure is then harvested by layer-by-layer peeling, producing multiple freestanding membranes with unprecedented throughput from a single wafer. Because 2D materials allow peeling at the interface without damaging the epilayer or the substrate, wafers can be reused for subsequent membrane production. Therefore, this work represents a meaningful step toward high-throughput and low-cost production of single-crystal membranes that can be heterointegrated.

physics.app-ph↗

arXiv2020

Multi-level Electro-thermal Switching of Optical Phase-Change Materials Using Graphene

Reconfigurable photonic systems featuring minimal power consumption are crucial for integrated optical devices in real-world technology. Current active devices available in foundries, however, use volatile methods to modulate light, requiring a constant supply of power and significant form factors. Essential aspects to overcoming these issues are the development of nonvolatile optical reconfiguration techniques which are compatible with on-chip integration with different photonic platforms and do not disrupt their optical performances. In this paper, a solution is demonstrated using an optoelectronic framework for nonvolatile tunable photonics that employs undoped-graphene microheaters to thermally and reversibly switch the optical phase-change material Ge$_2$Sb$_2$Se$_4$Te$_1$ (GSST). An in-situ Raman spectroscopy method is utilized to demonstrate, in real-time, reversible switching between four different levels of crystallinity. Moreover, a 3D computational model is developed to precisely interpret the switching characteristics, and to quantify the impact of current saturation on power dissipation, thermal diffusion, and switching speed. This model is used to inform the design of nonvolatile active photonic devices; namely, broadband Si$_3$N$_4$ integrated photonic circuits with small form-factor modulators and reconfigurable metasurfaces displaying 2$π$ phase coverage through neural-network-designed GSST meta-atoms. This framework will enable scalable, low-loss nonvolatile applications across a diverse range of photonics platforms.

physics.app-ph↗

arXiv2020

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries.

cond-mat.mtrl-sci↗

arXiv2019

Realization of 2D Crystalline Metal Nitrides via Selective Atomic Substitution

Two-dimensional (2D) transition metal nitrides (TMNs) are new members in the 2D materials family with a wide range of applications. Particularly, highly crystalline and large area thin films of TMNs are potentially promising for applications in electronic and optoelectronic devices; however, the synthesis of such TMNs has not yet been achieved. Here, we report the synthesis of few-nanometer thin Mo5N6 crystals with large area and high quality via in situ chemical conversion of layered MoS2 crystals. The structure and quality of the ultrathin Mo5N6 crystal are confirmed using transmission electron microscopy, Raman spectroscopy and X-ray photoelectron spectroscopy. The large lateral dimensions of Mo5N6 crystals are inherited from the MoS2 crystals that are used for the conversion. Atomic force microscopy characterization reveals the thickness of Mo5N6 crystals is reduced to about 1/3 of the MoS2 crystal. Electrical measurements show the obtained Mo5N6 samples are metallic with high electrical conductivity (~ 100 Ω sq-1), which is comparable to graphene. The versatility of this general approach is demonstrated by expanding the method to synthesize W5N6 and TiN. Our strategy offers a new direction for preparing 2D TMNs with desirable characteristics, opening a door for studying fundamental physics and facilitating the development of next generation electronics.

cond-mat.mtrl-sci↗

arXiv2018

Electrical homogeneity of large-area chemical vapor deposited multilayer hexagonal boron nitride sheets

Hexagonal boron nitride (h-BN) is a two dimensional (2D) layered insulator with superior dielectric performance that offers excellent interaction with other 2D materials (e.g. graphene, MoS2). Large-area h-BN can be readily grown on metallic substrates via chemical vapor deposition (CVD), but the impact of local inhomogeneities on the electrical properties of the h-BN and their effect in electronic devices is unknown. Here it is shown that the electrical properties of h-BN stacks grown on polycrystalline Pt vary a lot depending on the crystalline orientation of the Pt grain, but within the same grain the electrical properties of the h-BN are very homogeneous. The reason is that the thickness of the CVD-grown h-BN stack is different on each Pt grain. Conductive atomic force microscopy (CAFM) maps show that the tunneling current across the h-BN stack fluctuates up to 3 orders of magnitude from one Pt grain to another. However, probe station experiments reveal that the variability of electronic devices fabricated within the same Pt grain is surprisingly small. As cutting-edge electronic devices are ultra-scaled, and as the size of the metallic substrate grains can easily exceed 100 μm (in diameter), CVD-grown h-BN stacks may be useful to fabricate electronic devices with low variability.

cond-mat.mtrl-sci↗

arXiv2017

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators.

physics.optics↗

arXiv2016

Coupling-Enhanced Broadband Mid-Infrared Light Absorption in Graphene Plasmonic Nanostructures

Plasmons in graphene nanostructures show great promise for mid-infrared applications ranging from a few to tens of microns. However, mid-infrared plasmonic resonances in graphene nanostructures are usually weak and narrow-banded, limiting their potential in light manipulation and detection. Here we investigate the coupling among graphene plasmonic nanostructures and further show that by engineering the coupling, enhancement of light-graphene interaction strength and broadening of spectral width can be achieved simultaneously. Leveraging the concept of coupling, we demonstrate a hybrid 2-layer graphene nanoribbon array which shows 5 to 7% extinction within the entire 8 to 14 μm (~700 to 1250 cm-1) wavelength range, covering one of the important atmosphere "infrared transmission windows". Such coupled hybrid graphene plasmonic nanostructures may find applications in infrared sensing and free-space communications.

cond-mat.mes-hall↗

arXiv2011

A Localization Method for the Internet of Things

Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc, which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: 1) partition phase, in which the target region is split into small grids; 2) localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy.

cs.NI↗