Study of Ge-Rich Ge-Sb-Te Device-Dependent Segregation for Industrial Grade Embedded Phase-Change Memory

Elisa Petroni, Mario Allegra,Matteo Baldo,Luca Laurin,Andrea Serafini, Laurent Favennec, Latifa Desvoivres,Jury Sandrini, Christian Boccaccio, Yannick Le-Friec, Alain Ostrovsky, Pascal Gouraud, Aurore Bonnevialle,Rossella Ranica,Andrea Redaelli

PHYSICA STATUS SOLIDI-RAPID RESEARCH LETTERS(2024)

引用 0|浏览4
暂无评分
摘要
Ge-rich Ge-Sb-Te (GGST) alloys are the most promising materials for phase-change memory in embedded applications, being able to fulfill the tough data retention requirements of automotive and consumer markets. GGST alloys are sensitive to thermal budgets and spatial confinement; thus, memory device process integration and architecture can strongly impact their final electrical properties and reliability. Herein, exploiting a statistical methodology capable to extract quantitative metrics for evaluating by-process segregation, the inhomogeneity of out-of-fab GGST material in function of process parameters is studied such as architecture and alloy composition. The present results with the already known source of segregation, namely the back-end-of-line thermal budget, are compared providing a comprehensive description of the main modulating factors of segregation among these different process parameters. Herein, Ge-rich Ge-Sb-Te segregation in the function of different device and process parameters is studied. A statistical methodology, returning metrics to quantify material segregation, is exploited to analyze the effects of confinement and composition. Moreover, the present test case is compared with previous results, returning a comprehensive fit of all the known sources of segregation.-image (c) 2024 WILEY-VCH GmbH
更多
查看译文
关键词
compositional data analysis,electron energy loss spectroscopy,Ge-rich Ge-Sb-Te,phase-change memory,reliability,statistics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要