Low-loss phononic integrated circuits based on a silicon nitride-lithium niobate platform

Microwave-frequency acoustic waves in solids have emerged as a versatile platform for both classical and quantum applications. While phononic integrated devices and circuits are being developed on various material platforms, an ideal phononic integrated circuit (PnIC) platform should simultaneously support low-loss waveguide structures, high-quality-factor resonators, high-performance modulators, and efficient electromechanical transducers. Here, we establish a low-loss gigahertz-frequency PnIC platform based on patterned thin-film silicon nitride (SiN) on lithium niobate (LN) substrate. We develop low-loss PnIC building blocks including waveguides, directional couplers, and high-quality-factor (high-Q) ring resonators. As an application, we demonstrate a 1-GHz phononic oscillator based on a ring resonator, reaching a low phase noise of -159.0 dBc/Hz at a 100-kHz offset frequency. Our low-loss PnICs could meet the requirements in microwave acoustics, quantum phononics, and integrated hybrid systems combining phonons, photons, superconducting qubits, and solid-state defects.

FlowSSC: Universal Generative Monocular Semantic Scene Completion via One-Step Latent Diffusion

Semantic Scene Completion (SSC) from monocular RGB images is a fundamental yet challenging task due to the inherent ambiguity of inferring occluded 3D geometry from a single view. While feed-forward methods have made progress, they often struggle to generate plausible details in occluded regions and preserve the fundamental spatial relationships of objects. Such accurate generative reasoning capability for the entire 3D space is critical in real-world applications. In this paper, we present FlowSSC, the first generative framework applied directly to monocular semantic scene completion. FlowSSC treats the SSC task as a conditional generation problem and can seamlessly integrate with existing feed-forward SSC methods to significantly boost their performance. To achieve real-time inference without compromising quality, we introduce Shortcut Flow-matching that operates in a compact triplane latent space. Unlike standard diffusion models that require hundreds of steps, our method utilizes a shortcut mechanism to achieve high-fidelity generation in a single step, enabling practical deployment in autonomous systems. Extensive experiments on SemanticKITTI demonstrate that FlowSSC achieves state-of-the-art performance, significantly outperforming existing baselines.