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arXiv Machine Learning By Rodion Novkin, Hussam Amrouch

NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization

Read the original on arXiv Machine Learning →

The paper presents neural‑network (NN) transistor models that predict drain current and charges for a 3 nm nanosheet, and applies them to standard cell library characterization. The NN models achieve sub‑0.11 % error in delay and 0.76 % in total power across a wide range of circuits, while reducing characterization time by up to eight times compared to the industry‑standard Verilog‑A BSIM‑CMG model. The study also explores how NN size affects simulation precision and provides guidance for accurate circuit simulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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