VLSI Simulation with XGBoost Model

About the project

I’m modeling a VLSI circuit’s behaviour entirely through simulation and want to drive the process with an XGBoost Multi-Output Regressor. The goal is to replace portions of a traditional SPICE run with a machine-learning proxy that can predict multiple analog metrics at once (delay, power, noise, etc.) from layout-level or netlist-level features. Key details • Scope: pure simulation workflow; no verification or post-layout optimisation tasks are needed. • Model choice: XGBoost Multi-Output Regressor is fixed. • Critical tweak: I need a custom number of estimators rather than the default setting; help me decide and implement the optimal value through systematic tuning. What I expect from you 1. Clean, reproducible Python (preferably a Jupyter Notebook) that: – Pre-processes my CSV/netlist-derived dataset, – Trains and cross-validates the XGBoost model, – Outputs predictions for all target metrics in a form that can slot back into my current simulation flow. 2. Brief documentation describing feature engineering choices, chosen estimator count, and any other hyper-parameters. 3. A short note on how to extend the model to new process corners or larger datasets. Tools on my side I’m already set up with Python 3.10, scikit-learn, and xgboost on an Ubuntu workstation, so please align with that stack. Deliver the notebook, a requirements.txt (if extra libraries are needed), and a sample run showing the model converging and producing multi-output predictions. If everything runs smoothly on my end and the metrics meet or beat my current SPICE run time/accuracy trade-off, the job is finished.

Skills required