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The paper introduces a Bounded Dual-Loop Automated Schematic Synthesis Framework that converts natural language specifications into verified KiCad schematics and manufacturing deliverables for power electronics converters. It integrates six stages across three functional columns: requirement parsing and topology synthesis, evidence‑driven component selection with distributor APIs and datasheets, and modular control hardware integration with multi‑format deliverable generation. End‑to‑end case studies on Buck and Dual‑Active Bridge converters show significant improvements in component verification coverage, nominal value consistency, and SPICE simulation pass rates compared to a vanilla LLM baseline.
arXiv:2606. 01188v1 Announce Type: cross Abstract: Translating natural-language hardware requirements into correct printed circuit board (PCB) schematics remains difficult in embedded, IoT, and wearable development.
arXiv:2608. 13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.
arXiv:2607. 01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks.
Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks. However, their application to analog and mixed-signal domains remains limited by the lack of machine-readable representations of existing circuit design knowledge.
arXiv:2607. 03835v1 Announce Type: cross Abstract: Converting a SPICE netlist into a human-readable schematic is a longstanding problem in electronic design automation: simulators and machine-learning pipelines readily produce netlists, but designers reason about circuits through diagrams.