Graph neural networks (GNNs) are a class of deep learning algorithms that learn from
graphs, networks and relational data. They have found applications throughout the sciences …

This tutorial brief shows how Artificial Neural Networks (ANNs) can be used for the
optimization and automated design of analog and mixed-signal circuits. A survey of …

Design automation of analog circuits has long been sought. However, achieving robust and
efficient analog design automation remains challenging. This paper proposes a learning …

Process, voltage, and temperature (PVT) variations in chip fabrication or operation pose a
significant challenge to the robustness of analog integrated circuits. Existing design …

Designing analog integrated circuits (ICs), particularly sensors and reference circuits,
requires a significant amount of human expertise and time, largely due to the requirement of …

Robust design in analog integrated circuits (ICs) is intricate due to process variations,
culminating in notable performance uncertainties. Contemporary surrogate-based …

Design automation of analog circuits has been a long-standing challenge in the integrated
circuit field. Recently, multiple methods based on learning or optimization have …

Despite the effort of analog circuit design automation, currently complex analog circuit
design still requires extensive manual iterations, making it labor intensive and time …

With the CMOS technology advancing and the complexity of circuits growing, the demand for
analog/mixed-signal design automation tools is increasing quickly. Although some tools …

Despite recent advances in algorithms, such as the use of reinforcement learning, analog
circuit sizing optimization remains a challenging task that demands numerous circuit …