Due to the aggressive scaling of non-volatile memories, “charge-trap memories” such as MONOS-type memories become one of the most important targets. One of the merits of such MONOS-type memories is that they can trap charges inside atomic-scale defect sites in SiN layers. At the same time, however, charge traps with atomistic scale tend to induce additional large structural changes. Hydrogen has attracted a great attention as an important heteroatom in MONOS-type memories. We theoretically investigate the basic characteristics of hydrogen-defects in SiN layer in MONOS-type memories on the basis of the first-principles calculations. We find that SiN structures with a hydrogen impurity tend to reveal reversible structural change during program/erase operation.

In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online EM algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.