AsSe-free Si–Te–Ge chalcogenide glass OTS selectors designed by interpretable machine learning

Developing environmentally benign volatile switches for high-density memory arrays requires eliminating toxic arsenic and selenium without compromising thermal or electrical reliability. This study implements an interpretable machine learning framework to guide the compositional optimization of ternary chalcogenide systems. By leveraging SHAP feature analysis, we identified that a specific atomic balance is required to optimize network connectivity while maintaining high polarizability. Experimental validation of the ML-designed candidates revealed that a symmetric modifier ratio of Si10Ge10Te80 yields the most robust amorphous network, achieving a crystallization temperature of 235 °C and an optical bandgap of 1.05 eV. Electrical characterization of crossbar devices utilizing this composition demonstrates excellent volatile switching behaviors, characterized by a sharp threshold voltage of 1.25 V and an extremely low off-state leakage current of 5.3 × 10^-8 A. Furthermore, the optimized device exhibits superior dynamic performance, featuring a fast switching speed of approximately 12 ns and high endurance exceeding 108 cycles, significantly outperforming silicon-rich variants which failed near 105 cycles due to devitrification. The findings confirm that controlling the average valence electron concentration around 5.1 allows for the suppression of leakage pathways while ensuring sufficient thermal stability, proving that data-driven material selection can effectively engineer high-performance, eco-friendly components for next-generation storage class memory.

Keywords:
3D crosspoint memory; Thermal stability; Leakage current; Switching endurance; SHAP analysis