Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning.

It is always highly desired to have a well-defined relationship between the chemistry in semiconductor processing and the device characteristics. With the shrinkage of technology nodes in the semiconductors roadmap, it becomes more complicated to understand the relation between the device electrical characteristics and the process parameters such as oxidation and wafer cleaning procedures. In this work, we use a novel machine learning approach, i.

A novel mutation in the CD40 ligand gene in a Chinese boy with X-linked hyper-IgM syndrome.

X-linked hyper-IgM Syndrome (XHIGM) is caused by a mutation of CD40 ligand (CD40L), which is normally expressed on activated CD4+ T cells and is responsible for immunoglobulin class switching. A 7-year-old boy with recurrent sino-pulmonary infections since the age of 3 months had normal CD3+, CD4+, CD8+T lymphocytes, and CD19+B lymphocytes and NK cells, but significantly elevated IgM and extremely decreased IgG and IgA. Sequencing of genomic DNA revealed that the patient had a 34 base deletion in intron 3 and exon 4 of CD40L(g.