Utilising Scale Model Systems to Optimise Upstream Process Development

Author(s):  
Sally Grosvenor ◽  
Larissa Chirkova ◽  
Tatyana Mitina ◽  
Danny Voorhamme ◽  
Quang Doan ◽  
...  
2001 ◽  
pp. 315-325
Author(s):  
Anrew Daly ◽  
Otto Anker Nielsen

2004 ◽  
Vol 830 ◽  
Author(s):  
M. W. Stoker ◽  
T. P. Merchant ◽  
R. Rao ◽  
R. Muralidhar ◽  
S. Straub ◽  
...  

ABSTRACTSilicon nanocrystals can be used in non-volatile memory devices to reduce susceptibility to charge loss via tunnel oxide defects, allowing scaling to smaller sizes than possible with conventional Flash memory technology. In order to optimize device performance, it is desirable to maximize the nanocrystal density and surface coverage, while maintaining sufficient inter-crystallite separation to limit electron tunneling between adjacent crystallites. Ideally, crystallite densities in excess of 1012cm-2 with relatively narrow particle size distributions must be obtained, posing a significant challenge for process development and control. In order to facilitate development of such a process, a rate-expression-based model has been developed for the nucleation and growth of silicon nanocrystals on SiO2 in a CVD process. The model addresses the phenomena of nucleation, growth, and coalescence and includes the effects of exclusion zones surrounding the growing nuclei. The model uses a phenomenological expression to describe the nucleation rate and assumes that following nucleation, crystallite growth is dominated by gas-phase deposition processes, analogous to CVD of polycrystalline silicon. The model-predicted time-evolutions of crystallite densities and crystallite size distributions are consistent with experimental distributions as measured by Scanning Electron Microscopy (SEM). By coupling the model to a reactor-scale model of polysilicon CVD, it is possible to predict variations in the crystallite size distributions at various locations across a wafer as a function of reactor settings (temperature, pressure, flow rates, etc…). This in turn can be used for process control and optimization in order to ensure uniform deposition of nanocrystals in a large-scale manufacturing environment.


2015 ◽  
Vol 13 (8) ◽  
pp. 1094-1105 ◽  
Author(s):  
Markus Sack ◽  
Thomas Rademacher ◽  
Holger Spiegel ◽  
Alexander Boes ◽  
Stephan Hellwig ◽  
...  

2011 ◽  
Vol 10 (1) ◽  
pp. 34-39 ◽  
Author(s):  
Amy Kittredge Wood ◽  
Shrikanth Gowda ◽  
Laura Dinn ◽  
Janice Simler ◽  
Jane Ring ◽  
...  

2020 ◽  
Vol 314-315 ◽  
pp. 63-70
Author(s):  
Carolina Lanter ◽  
Malka Lev ◽  
Li Cao ◽  
Vakhtang Loladze

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Emily R. Pfeiffer ◽  
Jared R. Tangney ◽  
Jeffrey H. Omens ◽  
Andrew D. McCulloch

Cardiac mechanical contraction is triggered by electrical activation via an intracellular calcium-dependent process known as excitation–contraction coupling. Dysregulation of cardiac myocyte intracellular calcium handling is a common feature of heart failure. At the organ scale, electrical dyssynchrony leads to mechanical alterations and exacerbates pump dysfunction in heart failure. A reverse coupling between cardiac mechanics and electrophysiology is also well established. It is commonly referred as cardiac mechanoelectric feedback and thought to be an important contributor to the increased risk of arrhythmia during pathological conditions that alter regional cardiac wall mechanics, including heart failure. At the cellular scale, most investigations of myocyte mechanoelectric feedback have focused on the roles of stretch-activated ion channels, though mechanisms that are independent of ionic currents have also been described. Here we review excitation–contraction coupling and mechanoelectric feedback at the cellular and organ scales, and we identify the need for new multicellular tissue-scale model systems and experiments that can help us to obtain a better understanding of how interactions between electrophysiological and mechanical processes at the cell scale affect ventricular electromechanical interactions at the organ scale in the normal and diseased heart.


1995 ◽  
Vol 387 ◽  
Author(s):  
Zachary J. Lemnios

AbstractA new approach to semiconductor manufacturing is necessary to decouple end product cost from factory volume. The economy of scale model for manufacturing semiconductor integrated circuits is leading to factories that cost in excess of $1 billion and with costs anticipated to increase nonlinearly. Perhaps of greater concern is that process development and integration costs are of the same magnitude. The Advanced Research Projects Agency (ARPA) has funded several stand-alone research programs to explore new ways of designing and fabricating leading-edge semiconductors. The next steps are to tightly couple design and manufacturing through powerful new frameworks and embedded intelligence with tools. This new manufacturing capability based on flexible, intelligent equipment coupled to a powerful design framework could lead to a leading-edge manufacturing capability, but at a fraction of the cost of a conventional factory. Furthermore, this manufacturing model supports innovation and could lead to a new generation of ICs and systems. The key elements necessary to enable this new semiconductor fabrication environment is described.


Author(s):  
Hock Chuan Yeo ◽  
Jongkwang Hong ◽  
Meiyappan Lakshmanan ◽  
Dong-Yup Lee

ABSTRACTChinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.


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