A 5.3 GHz Al0.76Sc0.24N Two-Dimensional Resonant Rods Resonator with a kt2 of 23.9%

This work reports on the measured performance of an Aluminum Scandium Nitride (AlScN) Two-Dimensional Resonant Rods resonator (2DRR), fabricated by using a Sc-doping concentration of 24%, characterized by a low off-resonance impedance (~25 Ohm) and exhibiting a record electromechanical coupling coefficient (kt2) of 23.9% for AlScN resonators. In order to achieve such performance, we identified and relied on optimized deposition and etching processes for highly-doped AlScN films, aiming at achieving high crystalline quality, low density of abnormally oriented grains in the 2DRR's active region and sharp lateral sidewalls. Also, the 2DRR's unit-cell has been acoustically engineered to maximize the piezo-generated mechanical energy within each rod and to ensure a low transduction of spurious modes around resonance. Due to its unprecedented kt2, the reported 2DRR opens exciting scenarios towards the development of next generation monolithic integrated radio-frequency (RF) filtering components. In fact, we show that 5th-order 2DRR-based ladder filters with fractional bandwidths (BW) of ~11%, insertion-loss (I.L) values of ~2.5 dB and with >30 dB out-of-band rejections can now be envisioned, paving an unprecedented path towards the development of ultra-wide band (UWB) filters for next-generation Super-High-Frequency (SHF) radio front-ends.

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Sparse regression has recently been applied to enable transfer learning from very limited data. We study an extension of this approach to unsupervised learning -- in particular, learning word embeddings from unstructured text corpora using low-rank matrix factorization. Intuitively, when transferring word embeddings to a new domain, we expect that the embeddings change for only a small number of words -- e.g., the ones with novel meanings in that domain. We propose a novel group-sparse penalty that exploits this sparsity to perform transfer learning when there is very little text data available in the target domain -- e.g., a single article of text. We prove generalization bounds for our algorithm. Furthermore, we empirically evaluate its effectiveness, both in terms of prediction accuracy in downstream tasks as well as the interpretability of the results.