{"url":"/dataset/diffckt-official-dataset","name":"Diffckt Official Dataset","full_name":null,"description_markdown":"This Dataset is the official dataset accompanying the paper \"DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits\". Designed to advance automated analog circuit design, this dataset provides high-quality training and research resources for the field. Constructed based on the TSMC 65nm CMOS process, it contains over 400,000 pairs of amplifier structures and performance metrics .","description_withheld":null,"homepage":"https://github.com/CjLiu-NJU/DiffCkt","introduced_date":"2025-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/diffckt-a-diffusion-model-based-hybrid-neural","title":"DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits","first_author":null,"url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["Diffckt Official Dataset"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}