Electronic Design Automation (EDA) in analog Integrated Circuits (ICs) has been the focus of extensive research; however, unlike its digital counterpart, it has not achieved widespread adoption. In this systematic review, we discuss recent contributions in the last five years, highlighting methods that address data scarcity, topology exploration, process-voltage-temperature (PVT) variations, and layout parasitics. Our goal is to support researchers new to this domain by creating a comprehensive collection of references and practical application guidelines. We provide a methodological review of state-of-the-art machine learning (ML) approaches, including graph neural networks (GNNs), large language models (LLMs), and variational autoencoders (VAEs), which have been successfully applied to analog circuit sizing tasks. To the best of author’s knowledge, this is the first review to comprehensively explore the application of generative AI models in analog IC circuit design. We conclude that future research could focus on few-short learning with domain-adoption training of generative AI methods to simplify the design task such as human-tool interaction or guided design space exploration.

While circuit parameter optimization has matured significantly, the systematic discovery of novel circuit topologies remains a bottleneck in analog design automation. This work presents SymXplorer, an open-source Python framework designed for automated topology exploration through symbolic modeling of analog components. The framework enables a component-agnostic approach to architecture-level synthesis, integrating stability analysis and higher-order filter exploration within a streamlined API. By modeling non-idealities as lumped parameters, the framework accounts for physical constraints directly within the symbolic analysis. To facilitate circuit sizing, SymXplorer incorporates a multi-objective optimization toolbox featuring Bayesian optimization and evolutionary algorithms for simulation-in-the-loop evaluation. Using this framework, we conduct a systematic search for differential Common-Gate (CG) Bandpass Transimpedance Amplifier (TIA) topologies tailored for 5G New Radio (NR) Radio-over-Fiber applications. We propose a novel, orthogonally tunable Bandpass TIA architecture identified by the tool. Implementation in 65 nm CMOS technology demonstrates the efficacy of the framework. Post-layout results exhibit a tunable gain of 30–50 dBΩ, a center frequency of 3.5 GHz, and a tuning range of 500 MHz. The design maintains a power consumption of less than 400 μW and an input-referred noise density of less than 50 pA/$\sqrt{\mathrm{Hz}}$ across the passband. Finally, we discuss how this symbolic framework can be integrated into future agentic EDA workflows to further automate the analog design cycle. SymXplorer is open-sourced to encourage innovation in symbolic-driven analog design automation.