The continuous expansion of wireless communication technologies creates a pressing need for intelligent planning of a plethora of existing and emerging wireless services, in a timely and cost-effectiv...
Traditionally, chip-scale RF system design has been in the domain of the expert, dominated by thumb rules and trial and error techniques. Designing these ICs, that form the bedrock of the wireless net...
Attention mechanism is a key part in present-day LLM. This talk explores the attention mechanism for modeling of high-speed/high-frequency non-linear circuits. The talk starts with an introduction of ...
This talk will cover some of the promises and challenges involved with applying Generative AI and LLM’s to RF and Microwave Design. For many years, expert engineers have navigated complex EDA-tool flo...
This talk will highlight Cadence’s recent progress in applying AI to advance electromagnetic, signal integrity, and power integrity design. We will discuss how Large Language Models (LLMs) drive autom...
Traditional generative design was based on parametrized physics (including domain geometry) on which optimization procedures operate. In order to alleviate the cost of 3D high-fidelity simulations mod...
Prevailing Machine-Learning (ML)-based approaches leverage existing solvers and simulators to generate a rich set of solutions, which are then used to train a neural network to predict outcomes for un...
We develop and demonstrate a framework for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learn...
We propose a multimodal large language model for electromagnetic-wave reasoning and understanding. EM data (eg field maps and radiation patterns) are ingested as images and fused with text via a bridg...
We present the design and development of extended bandwidth, high-gain, and high-isolation InP distributed amplifiers in mm-wave and THz frequencies. We will review and discuss the input line loss, wh...