Plasmas and Controlled Nuclear Fusion

Contains reports on thirteen research projects split into three sections.National Science Foundation (Grant GK-2581

Context-Dependent Requirement for dE2F during Oncogenic Proliferation

The Hippo pathway negatively regulates the cell number in epithelial tissue. Upon its inactivation, an excess of cells is produced. These additional cells are generated from an increased rate of cell division, followed by inappropriate proliferation of cells that have failed to exit the cell cycle. We analyzed the consequence of inactivation of the entire E2F family of transcription factors in these two settings. In Drosophila, there is a single activator, dE2F1, and a single repressor, dE2F2, which act antagonistically to each other during development. While the loss of the activator dE2F1 results in a severe impairment in cell proliferation, this defect is rescued by the simultaneous loss of the repressor dE2F2, as cell proliferation occurs relatively normally in the absence of both dE2F proteins. We found that the combined inactivation of dE2F1 and dE2F2 had no significant effect on the increased rate of cell division of Hippo pathway mutant cells. In striking contrast, inappropriate proliferation of cells that failed to exit the cell cycle was efficiently blocked. Furthermore, our data suggest that such inappropriate proliferation was primarily dependent on the activator, de2f1, as loss of de2f2 was inconsequential. Consistently, Hippo pathway mutant cells had elevated E2F activity and induced dE2F1 expression at a point when wild-type cells normally exit the cell cycle. Thus, we uncovered a critical requirement for the dE2F family during inappropriate proliferation of Hippo pathway mutant cells

Methodology and practical application of an ArF resist model calibration

This paper focuses on a novel methodology for a fast and efficient resist model calibration. One of the most crucial parts when calibrating a resist model is the fitting of experimental data where up to 20 resist model parameters are varied. Although general optimization approaches such as simplex algorithms or genetic algorithms have proven suitable for many applications, they do not exploit specific properties of resist models. Therefore, we have developed a new strategy based on Design of Experiment methods which makes use of these specific characteristics. This algorithm will be outlined and then be demonstrated by applying it to the calibration of a Solid-C resist model for one ArF line/space resist. As characterizing dataset we chose: a) a Focus Exposure Matrix (FEM) for the dense array, b) linearity, c) OPE (optical proximity) curve and e) the MEEF (mask error enhancement factor) for a dense array. It turned out that a simultaneous fit of the complete data set wa s not possible by varying resist parameters only. Considering the optical parameters appeared to be crucial as well. Therefore the influence of the optical settings (illumination, projection, 3D mask effects) on the lithography process will be discussed at this point. Finally we obtained an excellent matching of model predictions and experimental results