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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Machine Learning Screening of Oxide Semiconductors for Energy-Efficient Back-End Transistors

Author(s)

Yash Gupta, Sai Siddhardha Singamaneni, Aryan Gupta

On-Site Speaker (Planned)

Sai Siddhardha Singamaneni

Abstract Scope

AI and data-center workloads are pushing chip energy demand toward unsustainable levels. Recent IEDM work showed amorphous indium oxide grown near 150 C and integrated into back-end transistors, where shorter data paths reduce switching energy. Because that result was experimental, any screening model should reproduce it before extrapolating. We test that directly. From JARVIS-DFT we use 25,194 oxides of real first-principles data and screen the 12,804 with computed bandgaps. A composition is favorable when a TBmBJ gap at or above 2.5 eV pairs with an electron effective mass at or below 0.4 m0. Among the 548 oxides with effective masses, 14 percent satisfy both criteria. A 145-feature composition-only classifier then ranks the space, and grouped five-fold cross-validation gives ROC-AUC 0.89, with precision of 1.0 at 20 and 0.70 at 50. The result is a validated, interpretable enrichment of oxide candidates for low-energy electronics.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Electronic Materials

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