A Compact Transformer-Based Fractional-N ADPLL in 10-nm FinFET CMOS

In this article, we introduce a fractional-N all-digital phase-locked loop (ADPLL) architecture based on a single LC-tank, featuring an ultra-wide tuning range (TR) and optimized for ultra-low area in 10-nm FinFET CMOS. Underpinned by excellent switches in the FinFET technology, a high turn-on/off capacitance ratio of LC-tank switched...

A 76.5-92.6 GHz CMOS LNA Using Two-Port kQ-Product Theory for Transformer Design

This letter presents a convenient approach based on the two-port kQ-product theory to analyze the influence of interwinding capacitive coupling on the efficiency of the transformer. It is demonstrated that a transformer with proper size can benefit from the interwinding capacitive coupling to maximize its efficiency at a desired frequency....

Sub-second photon dose prediction via transformer neural networks

Background: Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed. Purpose: We present a deep learning...