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AMSNet: Netlist Dataset for AMS Circuits

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arxiv 2405.09045 v2 pith:ATOKN7MG submitted 2024-05-15 cs.CV

classification cs.CV
keywords datasetamsnetcircuitdesignnetlistsautomaticgenerationmllms
verification ladder T0 review T1 audit T2 compute T3 formal
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Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled significant potential across various fields, suggesting their applicability in streamlining large-scale AMS IC design as well. A bottleneck in employing MLLMs for automatic AMS circuit generation is the absence of a comprehensive dataset delineating the schematic-netlist relationship. We therefore design an automatic technique for converting schematics into netlists, and create dataset AMSNet, encompassing transistor-level schematics and corresponding SPICE format netlists. With a growing size, AMSNet can significantly facilitate exploration of MLLM applications in AMS circuit design. We have made an initial set of netlists public, and will make both our netlist generation tool and the full dataset available upon publishing of this paper.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SABLE: An NDA-Safe Closed-Loop LLM Framework for Analog Circuit Optimization in Industrial EDA Flows

    cs.AR 2026-07 conditional novelty 7.0 of 10

    An NDA-safe scrubbed boundary lets cloud LLMs optimize analog circuits in real Cadence flows; multi-model PVT benchmarks show successful closure on LC-VCO (7/11) and two-stage op-amp (4/11) tasks.

  2. ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits

    cs.LG 2026-07 conditional novelty 6.0 of 10

    ParasGB releases the first public graph benchmark for predicting post-layout parasitic capacitance and resistance from pre-layout analog/SRAM circuit schematics.

  3. A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction

    cs.AI 2025-06

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