Field experiments were conducted in two seasons at the farm of Sokoine University of Agriculture in Morogoro, Tanzania (6.85°S; 37.64°E and 568 m.a.s.l.) during the short rain (November 2014 to January 2015) and the long rain (March to June 2015). The experiment was a split plot in randomized complete block design (RCBD) with 4 replicates. Weed management practices (herbicides, hoe weeding (3x) and weedy) were the main plot treatments; four rice genotypes (NERICA-1, NERICA-4, NERICA-7 and Mwangaza) were the subplots. Significant differences (P<0.05) were recorded on weed counts. Dominant weed groups as determined by Summed Dominance Ratio (SDR) in both experiments were broadleaf species (50.8%), sedges (25.2%) and grasses (24.0%). Post-emergence (8.6%) and hoe weeding (12.3%) significantly reduced weed dry biomass as compared to pre-emergence (17.8%) and weedy (61.3%) treatments in both experiments, respectively. Significant differences (P<0.

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As 248nm DUV lithography is pushed to the O.18tm generation with logic features O.14tm and below, process control requirements become severe. Previously acceptable exposure latitude variations due to substrate reflectivity have become unacceptable. Additionally, next generation 248nm steppers with extremely narrow band laser illumination cause significant increases in substrate interference effects. These factors create stringent requirements for antireflective coating (ARC) optimization. We present results of experimental work to fine tune inorganic ARC thickness and optical properties for subtractive and inlaid feature types at the 0.1 8p.m generation. This work focuses on cost and time effective single wafer ARC optimization methods for extension to 300mm wafer sizes. The methods include reflectometry, spectroscopic ellipsometry, generation oftest wafers with large film thickness uniformity and calibrated simulation.

An improved method is presented for the optimization of plasma deposited bottom inorganic anti-reflective coatings (ARCs). These ARCs have shown the capability to improve photolithography process margins through reduction of substrate reflectivity while meeting integration issues [1]. However, the ability to vary plasma ARC optical properties through deposition conditions has led to increased complexity of film stack optimization. We present simple but effective enhanced modeling methods for reducing the effort required to properly tune plasma ARC optical conditions and optimize complex film stacks incorporating these materials.

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