Microcontroller (MCU) performance screening ensures devices meet the maximum operating frequency Fmax specification. Speed Monitors (SMONs), implemented as ring oscillators, are used to estimate Fmax. Traditional machine learning (ML) models have been explored for this task but require extensive feature engineering and tuning. This work investigates Tabular Foundation Models, specifically TabPFN, for MCU performance prediction. TabPFN leverages in-context learning, enabling accurate inference without dataset-specific training. We evaluate its performance on a composite dataset combining four distinct MCU product families. Results show that TabPFN matches or exceeds baseline ML models while eliminating the need for manual optimization, offering a promising direction for efficient screening in semiconductor manufacturing with minimal human supervision.

Minimal Supervision, Maximum Accuracy: TabPFN for Microcontroller Performance Prediction / Bellarmino, Nicolò; Cantoro, Riccardo; Huch, Martin; Kilian, Tobias. - STAMPA. - (In corso di stampa). (Intervento presentato al convegno International Test Conference (ITC) 2025 tenutosi a San Diego, California (USA) nel 21-26 September, 2025).

Minimal Supervision, Maximum Accuracy: TabPFN for Microcontroller Performance Prediction

Nicolò Bellarmino;Riccardo Cantoro;
In corso di stampa

Abstract

Microcontroller (MCU) performance screening ensures devices meet the maximum operating frequency Fmax specification. Speed Monitors (SMONs), implemented as ring oscillators, are used to estimate Fmax. Traditional machine learning (ML) models have been explored for this task but require extensive feature engineering and tuning. This work investigates Tabular Foundation Models, specifically TabPFN, for MCU performance prediction. TabPFN leverages in-context learning, enabling accurate inference without dataset-specific training. We evaluate its performance on a composite dataset combining four distinct MCU product families. Results show that TabPFN matches or exceeds baseline ML models while eliminating the need for manual optimization, offering a promising direction for efficient screening in semiconductor manufacturing with minimal human supervision.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3002056