Browsing by Subject "Plasma-enhanced chemical vapor deposition"
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Item Deposition of epitaxial Si/Si-Ge/Ge and novel high-K gate dielectrics using remote plasma chemical vapor deposition(2003-08) Chen, Xiao, 1972-; Banerjee, Sanjay; Rabenberg, Llewellyn K.Both high quality epitaxial Si/Si-Ge/Ge films and novel high-k gate dieletrics have been deposited using an upgraded Remote Plasma Chemical Vapor Deposition (RPCVD) system. The upgrade of the RPCVD system consisted of two parts. The first part involved design and installation of a high-density inductively coupled plasma (ICP) source with its peripheral units, in place of an old surface analysis chamber. As a new deposition chamber, this chamber is capable of generating high plasma density with significantly lower ion energy. The second part involved modification of an existing deposition chamber for high-k film deposition. With the final integration of the new RPCVD system, better interfacial quality, lower thermal budget, less contamination and autodoping, and easier process control are expected. Experiments on epitaxial Si growth were conducted in the new RPCVD chamber in order to characterize growth dependence on different processing parameters. The process was extended to epitaxial Ge/SiGe films on Si that are beyond their Critical Layer Thickness (CLT). High quality epitaxial Si1-xGex (x>0.5) and Ge metastable films were achieved with epitaxial thicknesses at least 5 times higher than the corresponding CLT. High-k gate dielectric growth was performed in another modified deposition chamber. Low temperature RPCVD HfO2 was obtained with excellent physical and electrical characteristics. Finally, epitaxial Ge/SiGe and novel high-k dielectrics processes were integrated to fabricate MOS capacitors. These capacitors, with excellent structural and electrical properties, significantly increased the potential to fabricate high channel mobility MOSFET devices using RPCVD.Item Gaussian process regression for virtual metrology of microchip quality and the resulting strategic sampling scheme(2017-09-15) Darwin, Tyler Jackson; Djurdjanovic, DraganManufacturing of integrated circuits involves many sequential processes, often ex- ecuted to nanoscale tolerances, and the yield depends on the often unmeasured quality of intermediate steps. In the high-throughput industry of fabricating microelectronics on semi-conducting wafers, scheduling measurements of product quality before the electrical test of the complete IC can be expensive. We therefore seek to predict metrics of product quality based on sensor readings describing the environment within the relevant tool during the processing of each wafer, or to apply the concept of virtual metrology (VM) to monitor these intermediate steps. We model the data using Gaussian process regression (GPR), adapted to simultaneously learn the nonlinear dynamics that govern the quality characteristic, as well as their operating space, expressed by a linear embedding of the sensor traces’ features. Such Bayesian models predict a distribution for the target metric, such as a critical dimension, so one may assess the model’s credibility through its predictive uncertainty. Assuming measurements of the quality characteristic of interest are budgeted, we seek to hasten convergence of the GPR model to a credible form through an active sampling scheme, whereby the predictive uncertainty informs which wafer’s quality to measure next. We evaluate this convergence when predicting and updating online, as if in a factory, using a large dataset for plasma-enhanced chemical vapor deposition (PECVD), with measured thicknesses for ~32,000 wafers. By approximately optimizing the information extracted from this seemingly repetitive data describing a tightly controlled process, GPR achieves ~10% greater accuracy on average than a baseline linear model based on partial least squares (PLS). In a derivative study, we seek to discern the degree of drift in the process over the several months the data spans. We express this drift by how unusual the relevant features, as embedded by the GPR model, appear as the in- puts compensate for degrading conditions. This method detects the onset of consistently unusual behavior that extends to a bimodal thickness fault, anticipating its flagging by as much as two days.Item Low temperature doped and undoped expitaxial Si and Si1-xGex growth(1995-05) Li, Chi-nan Brian; Not available