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A spatial and causal classification of process error provides opportunities for the accurate determination and efficient management of process error budget. Traditional metrology is posed with this dilemma: variability sampling requires cheap, highly repeatable metrology, such as electrical measurements, which also confound error sources of the variability sampled. In response, statistical metrology has been proposed as a novel combination of cost-effective metrology with subsequent statistical or experimental data processing to provide a technique that is capable of error decomposition into equipment causes. The methodology, consisting of 1) reticle and experiment design, 2) data filtering, and 3) error budget formulation, is presented and is general to a short-loop thin-film patterning sequence. A .35-[[micro]meter] polygate patterning sequence is chosen to demonstrate this technique. Reticle design and statistical filtering has been presented in a previous publication, and is summarized here. The second causal data filter is presented in this work. Aided by additional experimentation, a physical filter decomposes the separate contributions and interactions of the reticle and stepper. A portion of the error budget is calculated, including the effects of spatial correlation. The results of decomposition yields a numerical metric for equipment and process manufacturability. Results are presented that illustrate the use of the manufacturability metric in equipment selection and process design.

Modern submicron processes are more sensitive to both random and systematic wafer-level process variation than ever before. Given the dimensional control limitations of new technologies, the amount of wafer-to-wafer and within wafer nonuniformity of many steps is becoming a significant fraction of the total error budget, which already includes the usual step-to-step allocations. However, a significant portion of the total observed variability is systematic in nature. Accordingly, particle defects may not continue to dominate parametric yield loss without improved understanding of parametric variations. In this paper, we demonstrate the use of short-loop electrical metrology to carefully characterize and decouple wafer-level variability of several critical processing steps. More specifically, we present our method and give results obtained from variability analyses for lithography critical dimension (CD) and inter-level dielectric (ILD) thickness control. Using statistically designed experiments and dedicated test structures, the main factors affecting dielectric thickness variability has been identified. The systematic variability from a wafer stepper has been extracted using a physically based statistical data filter. Once isolated, the deterministic variability can be modeled and controlled to enhance process and circuit design for manufacturability (DFM). We hope that in the future this work will be coupled with novel DFM-oriented CAD tools that encapsulate this information in a fashion that makes it useful to process and circuit designers.

A design methodology for obtaining optimized, deep-submicrometer complementary metal oxide semiconductor devices for low temperature operation is presented. A new simulator that maximizes the balance between performance and reliability based on scaling criteria and process/device parameters is also introduced. It facilitates the design and process optimization of the device. A discussion of simulation and experimental results are also given.

Features a study which applied chromeless phase-shift masks to sub-100 nanometer-gate-length fully depleted silicon on insulator complementary metal oxide semiconductor transistor fabrication. Experimentation for chromeless phase-shift mask application; Imaging and pattern transfer of dense grating features with dark field chromeless phaseshift; Conclusion.

Branch bark extract (BBE) derived from the mulberry cultivar Husang 32 (Morus multicaulis L.) with aqueous alcohol solution has been investigated as an inhibitor of α-glycosidase in vitro. Mulberry BBE was orally administered to STZ-induced diabetic mice for three weeks, and it improved the weight gain and ameliorated the swelling of liver and kidney in diabetic mice. Obviously, mulberry BBE not only can reduce the abnormally elevated levels of serum insulin and ameliorate insulin resistance induced by STZ, but also it regulates dyslipidemia in diabetic mice. To understand this therapeutic effect and the regulatory mechanisms of BBE in diabetic mice, a qRT-PCR experiment was performed, indicating that the mulberry BBE can regulate the mRNA expression of glycometabolism genes in diabetic mice, including glucose-6-phosphatase (G6Pase), glucokinase (GCK), and phosphoenolpyruvate carboxykinase (PEPCK), thereby regulating sugar metabolism and reducing the blood glucose level in diabetic mice. The mulberry BBE can increase the mRNA expression of the genes Ins1, Ins2 and pancreatic duodenal homeobox-1 (PDX-1) and may decrease the insulin resistance in diabetic mice. Those results provide an important basis for making the best use of mulberry branch resources and producing biomedical drugs with added value.