Abstract
<jats:p>This work systematically evaluates the capability of generative large language models (LLMs), specifically GPT-4o, to support the full design, simulation, and optimization workflow of low-dropout (LDO) linear voltage regulators. The study covers four core design phases: pre-design specification mapping, transistor-level circuit topology generation, SPICE simulation guidance, and postsimulation performance fine-tuning, with an extended investigation into the integration of magnetic inductive components within the LDO signal path. GPT-4o autonomously proposes a single-stage differential-pair error amplifier architecture with thin-oxide MOS transistors, provides sizing guidance for the PMOS pass element, and recommends passive compensation networks to secure closed-loop stability. The LDO testbench adheres to low-voltage portable electronics specifications: an input range of 0.8-1.2 V, tunable 0.7-1.1 V output, maximum 250 mA load current, and integrable output capacitance below 10 nF. Transient and small-signal AC SPICE simulations validate LLM-assisted circuit implementations, quantifying settling time reduction via compensation capacitors and verifying adequate phase margin across operating bandwidth. A key novel extension explores three distinct inductor placement schemes, input-side supply filtering, out-of-loop LC output filtering, and inductive loading embedded within the feedback divider, rooted in Maxwell’s electrodynamic principles and MOSFET small-signal device physics. Comparative Bode and output impedance analysis reveals that inductors inserted inside the feedback path introduce resonant complex poles, severe gain peaking, and degraded phase margin, while inductors placed external to the feedback sensing tap preserve regulator stability while suppressing high-frequency electromagnetic interference. Despite robust performance in topology drafting and simulation instruction, GPT-4o exhibits notable limitations: it occasionally omits critical passive components matching design constraints, generates syntactically flawed SPICE netlists for custom transistor subcircuits, and fails self-correction for unconventional magnetic load configurations. Overall, this work demonstrates that LLMs function as powerful auxiliary engineering assistants to accelerate iterative analog design, though rigorous human crossverification of component sizing, loop stability, and passive network topology remains mandatory for reliable LDO implementation, particularly when integrating inductive magnetic elements.</jats:p>