Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
FanoutNet: A Neuralized PCB Fanout Automation Method Using Deep Reinforcement Learning
Authors: Haiyun Li, Jixin Zhang, Ning Xu, Mingyu Liu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world industrial PCB benchmarks demonstrate that our approach achieves 100% routability in all industrial cases and improves wire length by an average of 6.8%, which makes a significant improvement compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 School of Information Engineering, Wuhan University of Technology, Wuhan, China 2 School of Computer Science, Hubei University of Technology, Wuhan, China 3 Wuhan Research Institute, Huawei Device Co., Ltd., Wuhan, China |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. It only provides a link to a dataset: 'PCB dataset was acquired at https://github.com/aspdacsubmission-pcb-layout/PCBBenchmarks/'. |
| Open Datasets | Yes | We build two benchmark datasets consisting of eleven open-source PCB cases1 and five industrial PCB cases...1PCB dataset was acquired at https://github.com/aspdacsubmission-pcb-layout/PCBBenchmarks/ |
| Dataset Splits | No | The paper mentions 'Pre-Routability (Pre-RT) to compute approximate routability for RL training' but does not provide specific dataset split information (percentages, sample counts, or explicit methodology) for training, validation, or testing splits. |
| Hardware Specification | Yes | The experiments are performed on a 64-bit Windows workstation with AMD Ryzen 7 5800X 8-Core Processor 3.79 GHz, and 64 GB RAM. |
| Software Dependencies | No | The paper states 'Our method is implemented in Python with Pytorch for reinforcement learning, and C++ with the mingw64 compiler for PCB router,' but does not provide specific version numbers for PyTorch or the mingw64 compiler, which is necessary for reproducibility. |
| Experiment Setup | Yes | Our PPO hyperparameters, K epochs and the learning rate of policy and value model, are 20, 3e-4 and 1e-3, respectively. |