arXiv subjects

Junwei Cao

Publications and source records attributed to Junwei Cao.

8 recordsLinked to original sources

arXiv2026

Strain-released epitaxy of GaN enabled by compliant single-crystalline metal foils

Heteroepitaxy conventionally relies on rigid crystalline substrates, implicitly assuming that lattice and thermal mismatch must be accommodated within the epitaxial layer, leading to residual strain and defects that worsen with increasing substrate size. Here we demonstrate a substrate-mediated strain-partitioning regime in which lattice and thermal mismatch are preferentially partitioned into the substrate rather than stored in the epitaxial layer. We report the epitaxial growth of single-crystalline GaN on mechanically compliant yet crystallographically ordered single-crystalline copper foils. Atomic-resolution microscopy, geometric phase analysis and density functional theory reveal that mismatch-induced stress is primarily screened by elastic deformation of the Cu lattice, accompanied by localized interfacial slip confined to a few atomic layers, leaving the AlN and GaN epilayers nearly strain-free despite large nominal mismatch. Leveraging this strain-released epitaxial platform, we further demonstrate dense GaN micro-light-emitting diode arrays that benefit from efficient vertical electrical conduction and thermal dissipation enabled by the metallic substrate. By establishing compliant single-crystal metal foils as a new substrate class, this work identifies mechanical contrast as an underexplored governing parameter in heteroepitaxial design, with implications extending beyond GaN.

cond-mat.mtrl-sci↗

arXiv2026

Scalable Solar-Blind Imaging Enabled by Single-Crystalline Beta-Ga2O3 Membranes on Silicon Backplanes

Ultrawide-bandgap semiconductors are attractive for solar-blind ultraviolet (UV) detection owing to their intrinsically low noise and high spectral selectivity, yet their deployment in large-area, high-density electronic imaging systems remains limited by a fundamental trade-off between material quality, device speed, and compatibility with high-density planar silicon readout circuits. Here, we report a membrane-enabled integration platform based on transferable single-crystalline beta-Ga2O3 that overcomes these constraints at the system level. By exploiting the weak interplanar bonding of beta-Ga2O3 (100) plane, we obtain wafer-scale freestanding single-crystalline membranes that enable vertically integrated photodiodes with sub-microsecond, non-persistent photoresponse and high UV-visible rejection. Crucially, we introduce a stitching-based membrane assembly strategy that decouples array resolution from the size of the source single-crystalline substrate, allowing high-resolution photodetector arrays to be integrated onto silicon thin-film-transistor backplanes. The modular assembled active-matrix UV imaging arrays exhibit uniform solar-blind response without image lag, in stark contrast to arrays based on amorphous or polycrystalline films. Beyond beta-Ga2O3, this membrane-enabled and stitching-based modular integration strategy provides a general route toward high-speed, high-resolution electronic imaging systems using transferable single-crystalline semiconductors.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelectricity in 6 Angstrom-Thick Two-dimensional Ga$_2$O$_3$

Atomic-scale ferroelectric thin films hold great promise for high-density, low-power applications but face stability and voltage scaling challenges at extreme thinness. Here, we demonstrate ferroelectricity in single-crystalline two-dimensional (2D) Ga$_2$O$_3$, an ultra-wide-bandgap semiconductor, at just 6 angstrom thickness, exhibiting exceptional retention and thermal stability. We show that epitaxial beta-Ga$_2$O$_3$ can be exfoliated down to a half-unit cell thickness via a self-limiting mechanism, enabling a biaxial strain-induced phase transition into a novel ferroelectric layered structure. Strain modulation enables the reduction of polarization switching voltage to 0.8 V, meeting CMOS voltage scaling requirements. Theoretical calculations reveal that switching is driven by covalent bond reconstruction, effectively countering depolarization and enhancing stability. Additionally, we integrate ferroelectric 2D Ga$_2$O$_3$ onto silicon using a low-temperature, back-end-of-line-compatible process. This work advances the exploration of sub-nanometer ferroelectrics, paving the way for high-density, low-power, non-volatile applications seamlessly integrated with advanced silicon technology.

cond-mat.mtrl-sci↗

arXiv2022

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

Computational task offloading based on edge computing can deal with the performance bottleneck of traditional cloud-based systems for Internet of things (IoT). To further optimize computing efficiency and resource allocation, collaborative offloading has been put forward to enable the offloading from edge devices to IoT terminal devices. However, there still lack incentive mechanisms to encourage participants to take over tasks from others. To counter this situation, this paper proposes a distributed computational resource trading strategy addressing multiple preferences of IoT users. Unlike most existing works, the objective of our trading strategy comprehensively considers different satisfaction degrees with task delay, energy consumption, price, and user reputation of both requesters and collaborators. The system design uses blockchain to enhance the decentralization, security, and automation. Compared with the trading method based on classical double auction matching mechanism, our trading strategy has more tasks offloaded and executed, and the trading results are friendlier to collaborators with good reputation.

cs.DC↗

arXiv2021

Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?

As a model-free optimization and decision-making method, deep reinforcement learning (DRL) has been widely applied to the filed of energy management in energy Internet. While, some DRL-based energy management schemes also incorporate the prediction module used by the traditional model-based methods, which seems to be unnecessary and even adverse. In this work, we implement the standard energy management scheme with prediction using supervised learning and DRL, and the counterpart without prediction using end-to-end DRL. Then, these two schemes are compared in the unified energy management framework. The simulation results demonstrate that the energy management scheme without prediction is superior over the scheme with prediction. This work intends to rectify the misuse of DRL methods in the field of energy management.

eess.SY↗

arXiv2021

Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-world applications but still faces challenge with system scalability, e.g., the high cost of communication overhead during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households. This scheme preserves household privacy completely and significantly reduces communication cost. Simulation experiments demonstrate that the proposed framework achieves comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints.

cs.MA↗

arXiv2019

Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors

In this paper, we study an application of deep learning to the advanced LIGO and advanced Virgo coincident detection of gravitational waves (GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta (2017) for multi-inputs of network detectors. Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network (DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio (SNR) of network detectors $\geq$ 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance $D$, right ascension $α$, and declination $δ$ of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23%. Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors; these results reveal that a larger number of network detectors results in a better source location.

astro-ph.IM↗

arXiv2013

Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used.

astro-ph.IM↗