In-Context Learning for Microcontroller Performance Screening Using Tabular Foundation Models

Microcontroller (MCU) performance screening ensures that devices meet critical specifications, such as maximum operating frequency (Fmax). On-chip Speed Monitors (SMONs), implemented as ring oscillators, provide process-correlated signals that can be used to estimate Fmax via machine learning (ML). However, traditional ML models require substantial domain expertise, extensive feature engineering, hyperparameter tuning, and dataset-specific training, limiting their scalability and generalization. In this preliminary study, we explore the use of TabPFN, a pre-trained Tabular Foundation Model (TabFM) based on In-Context Learning (ICL), for MCU performance prediction. TabPFN eliminates the need for task-specific training or tuning by conditioning directly on labeled examples provided at inference time, enabling few-shot and zero-shot learning. We evaluate TabPFN on two distinct MCU datasets and compare its performance with conventional ML models, including tree-based and linear approaches. Our results show that TabPFN consistently achieves competitive accuracy with minimal human supervision, demonstrating its potential as a fast, generalizable, and low-maintenance alternative for performance screening in semiconductor manufacturing.