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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
C-Band Lithium Niobate on Silicon Carbide SAW Resonator W...
Tzu-Hsuan Hsu, Joshua Campbell, Jack Kramer, Sinwoo Cho, Ming-Hu · 2024-02-27 · via eess.SP updates on arXiv.org

In this work, we demonstrate a C-band shear-horizontal surface acoustic wave (SH-SAW) resonator with high electromechanical coupling (kt2) of 22% and a quality factor (Q) of 565 based on a thin-film lithium niobate (LN) on silicon carbide (SiC) platform, featuring an excellent figure-of-merit (FoM = kt2*Q ) of 124 at 6.5 GHz, the highest FoM reported in this frequency range. The resonator frequency upscaling is achieved through wavelength ($λ$) reduction and the use of thin aluminum (Al) electrodes. The LN/SiC waveguide and synchronous resonator design collectively enable effective acoustic energy confinement for a high FoM, even when the normalized thickness of LN approaches a scale of 0.5$λ$ to 1$λ$. To perform a comprehensive study, we also designed and fabricated five additional resonators, expending the $λ$ studied ranging from 480 to 800 nm, in the same 500 nm-thick transferred Y-cut thin-film LN on SiC. The fabricated SH-SAW resonators, operating from 5 to 8 GHz, experimentally demonstrate a kt2 from 20.3% to 22.9% and a Q from 350 to 575, thereby covering the entire C-band with excellent performance.