Thin-Film Lithium Niobate Acoustic Filter at 23.5 GHz with 2.38 dB IL and 18.2% FBW

This work reports an acoustic filter at 23.5 GHz with a low insertion loss (IL) of 2.38 dB and a 3-dB fractional bandwidth (FBW) of 18.2%, significantly surpassing the state-of-the-art. The device leverages electrically coupled acoustic resonators in 100 nm 128{\deg} Y-cut lithium niobate (LiNbO3) piezoelectric thin film, operating in the first-order antisymmetric (A1) mode. A new film stack, namely transferred thin-film LiNbO3 on silicon (Si) substrate with an intermediate amorphous silicon (a-Si) layer, facilitates the record-breaking performance at millimeter-wave (mmWave). The filter features a compact footprint of 0.56 mm2. In this letter, acoustic and EM consideration, along with material characterization with X-ray diffraction and verified with cross-sectional electron microscopy are reported. Upon further development, the reported filter platform can enable various front-end signal-processing functions at mmWave.

Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts

This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the model from a finite set of human-generated data. We argue that using limited demonstrations to kick-start the questioner is insufficient due to the large policy search space. Inspired by a recently proposed information theoretic approach, we develop two analytic experts to serve as a source of high-quality demonstrations for imitation learning. We then take advantage of reinforcement learning to refine the model towards the goal-oriented objective. Experimental results on the GuessWhat?! dataset show that our method has the combined merits of imitation and reinforcement learning, achieving the state-of-the-art performance.