Browsing by Author "Huang, Shengxi"

Now showing 1 - 7 of 7
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    Exploring Topological Semi-Metals for Interconnects
    (MDPI, 2023) Kundu, Satwik; Roy, Rupshali; Rahman, M. Saifur; Upadhyay, Suryansh; Topaloglu, Rasit Onur; Mohney, Suzanne E.; Huang, Shengxi; Ghosh, Swaroop
    The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level interconnect design using NbAs, a topological semi-metal with high electron mobility at the nanoscale, and demonstrated its advantages over Cu in terms of resistivity, propagation delay, and slew rate. Our simulations revealed that NbAs could reduce the propagation delay by up to 35.88%. We further evaluated the potential system-level performance gain for NbAs-based interconnects in cache memories and observed an instructions per cycle (IPC) improvement of up to 23.8%.
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    High-Dimensional Spectroscopy Analysis with Machine Learning Techniques
    (2023-04-04) Wang, Ziyang; Huang, Shengxi
    High-dimensional spectroscopy often provides rich information. Raman spectroscopy is a non-destructive molecular sensing method. However, Raman signals in bio-samples are hard to interpret, due to the high dimensionality. Measurement of optical spectroscopy is also complicated and requires high-end instrumentations and intricate data analysis techniques. Machine learning methods offer great opportunities to extract subtle and deep information in high-dimensional spectra. They can also assist measurement of complex optical spectroscopy of materials with simpler optical setups. In this work, we develop a platform that enables rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in animal brains. The method facilitates the study of AD and can be extended to other tissues, biofluids, and for various other diseases. We also propose a computational reflectometry approach based on a deep learning model called ReflectoNet. It predicts complex refractive indices of thin films on top of nontrivial substrates from reflectance spectra, which was not feasible previously.
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    Manufacturing Chip-Scale 2D Monolayer Single Crystals and Engineering Quantum Emission in 2D Materials
    (2024-08-05) Wu, Wenjing; Huang, Shengxi; Kon, Junichiro
    Two-dimensional (2D) materials and their van der Waals (vdW) heterostructures continue to reveal unconventional electronic, optical, and magnetic phenomena closely tied to their dimensionality. In the first part of this thesis, we have demonstrated a facile method for producing uniform, large-area, and crack-free single-crystal transition metal dichalcogenide (TMD) monolayers and artificial structures: wafer-bonder-assisted transfer (WBAT). Compared with single-crystal monolayers produced via traditional Scotch tape exfoliation, the WBAT method can produce flakes that are larger in area by > 10^6 times with almost no cracks. In the second part, we focus on the creation of single photon emitters in the WSe2 and WS2 thin flakes, with defect and strain engineering. Our results show a nearly ideal single-photon purity with g^2(0) = 0.03 through effective spectral background suppression.
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    Measuring complex refractive index through deep-learning-enabled optical reflectometry
    (IOP Publishing, 2023) Wang, Ziyang; Lin, Yuxuan Cosmi; Zhang, Kunyan; Wu, Wenjing; Huang, Shengxi
    Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet. Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.
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    Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications
    (MDPI, 2023) Ranasinghe, Jeewan C.; Wang, Ziyang; Huang, Shengxi
    Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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    Topological Photonic Devices in the UV-visible Spectrum Based on the III-N Wide Bandgap Semiconductor Platform
    (2024-04-19) Li, Tao; Zhao, Yuji; Huang, Shengxi; Chen, Songtao
    Topological photonics, renowned for the edge/interface states resistant to local defects and back-scattering, can be a promising solution for ensuring the stability in integrated photonic platforms and has already found applications in lasers and quantum photonic circuits. However, existing topological photonic demonstrations have primarily operated in the microwave or near-infrared spectrum due to material and nanofabrication limitations. In this thesis, we break through this wavelength barrier and extend the limit into UV-visible spectrum by implementing topological photonics on the III-N wide bandgap semiconductor platform. In the first part of the thesis, we devise a 1D topological photonic cavity fabricated from a gallium nitride on silicon (GaN-on-Si) wafer. The designed cavity has a single resonance mode around the wavelength of 800 nm and shows a simulated quality factor (Q) around 1600. Based on the non-zero second-order susceptibility of the GaN, we further demonstrate the second harmonic generation (SHG) from the 1D topological photonic cavity and reveal the power dependence and polarization dependence of the cavity-based SHG. The second part of the thesis focuses on the design of topological photonic routing devices in the visible spectrum based on 2D photonic crystals (PC) made of hexagonal boron nitride (h-BN). Interfacing 2D h-BN PCs with distinct topological phases gives rise to topological edge states supporting polarization-resolved unidirectional propagation. Through meticulous design of the interfaces’ shape, we demonstrate ultra-compact topological photonic routers. These routers feature 6 input/output ports within a 10 µm × 10 µm footprint and showcase a simulated crosstalk extinction ratio exceeding 15 dB. The results from this thesis underpin the UV-visible topological photonics based on the III-N wide bandgap semiconductor platform and can potentially benefit the design of high-performance integrated photonic devices in the UV-visible spectrum by leveraging the unique properties of photonic topology.
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    Topology stabilized fluctuations in a magnetic nodal semimetal
    (Springer Nature, 2023) Drucker, Nathan C.; Nguyen, Thanh; Han, Fei; Siriviboon, Phum; Luo, Xi; Andrejevic, Nina; Zhu, Ziming; Bednik, Grigory; Nguyen, Quynh T.; Chen, Zhantao; Nguyen, Linh K.; Liu, Tongtong; Williams, Travis J.; Stone, Matthew B.; Kolesnikov, Alexander I.; Chi, Songxue; Fernandez-Baca, Jaime; Nelson, Christie S.; Alatas, Ahmet; Hogan, Tom; Puretzky, Alexander A.; Huang, Shengxi; Yu, Yue; Li, Mingda
    The interplay between magnetism and electronic band topology enriches topological phases and has promising applications. However, the role of topology in magnetic fluctuations has been elusive. Here, we report evidence for topology stabilized magnetism above the magnetic transition temperature in magnetic Weyl semimetal candidate CeAlGe. Electrical transport, thermal transport, resonant elastic X-ray scattering, and dilatometry consistently indicate the presence of locally correlated magnetism within a narrow temperature window well above the thermodynamic magnetic transition temperature. The wavevector of this short-range order is consistent with the nesting condition of topological Weyl nodes, suggesting that it arises from the interaction between magnetic fluctuations and the emergent Weyl fermions. Effective field theory shows that this topology stabilized order is wavevector dependent and can be stabilized when the interband Weyl fermion scattering is dominant. Our work highlights the role of electronic band topology in stabilizing magnetic order even in the classically disordered regime.