Yield analysis by poisson yield model based on the defect analysis with derivative method

2014 ◽  
Author(s):  
W. Praepattharapisut ◽  
W. Pengchan ◽  
T. Phetchakul ◽  
A. Poyai
Keyword(s):  
Defect Analysis ◽  
Yield Model ◽  
Yield Analysis ◽  
Model Based ◽  
Derivative Method ◽  
Poisson Yield Model

Yield Analysis Based on the Defect Analysis with Derivative Method

2015 ◽  
Vol 781 ◽  
pp. 160-163 ◽  
Author(s):  
Warakorn Praepattharapisut ◽  
Weera Pengchan ◽  
Toempong Phetchakul ◽  
Amporn Poyai
Keyword(s):  
Silicon Wafer ◽  
Calculated Data ◽  
Cmos Technology ◽  
Defect Analysis ◽  
Yield Analysis ◽  
Current Voltage ◽  
Capacitance Voltage ◽  
Derivative Method ◽  
Actual Yield ◽  
Characteristic Measurement

This paper presented the corresponding between the yield equation prediction from Poisson, Murphy with wafer actual yield on the silicon wafer with 0.8 μm CMOS technology. The defect analysis with derivative method, current - voltage and capacitance-voltage of diode characteristic measurement, is used to define the defect in p-n junction on silicon wafer. The different sampling numbers of chips are used to calculate the yield. Finally the calculated data and actual would be compared and found that at sampling number is 25, the tolerance from actual yield is less than 3%.

A methodology for model-based greenhouse design: Part 3, sensitivity analysis of a combined greenhouse climate-crop yield model

2011 ◽  
Vol 110 (4) ◽  
pp. 396-412 ◽  
Author(s):  
B.H.E. Vanthoor ◽  
E.J. van Henten ◽  
C. Stanghellini ◽  
P.H.B. de Visser
Keyword(s):  
Sensitivity Analysis ◽  
Crop Yield ◽  
Yield Model ◽  
Greenhouse Climate ◽  
Model Based

Rainstorm‐generated sediment yield model based on soil moisture proxies ( SMP )

2020 ◽  
Vol 34 (16) ◽  
pp. 3448-3463
Author(s):  
Sushindra Kumar Gupta ◽  
Pushpendra K. Singh ◽  
Jaivir Tyagi ◽  
Gunwant Sharma ◽  
Ajay Singh Jethoo
Keyword(s):  
Soil Moisture ◽  
Sediment Yield ◽  
Yield Model ◽  
Model Based

scholarly journals Association mapping for resin yield in Pinus roxburghii Sarg. using microsatellite markers

2014 ◽  
Vol 63 (1-6) ◽  
pp. 253-266 ◽  
Author(s):  
Anita Rawat ◽  
S. Barthwal ◽  
H. S. Ginwal
Keyword(s):  
Association Mapping ◽  
Ssr Markers ◽  
Association Analysis ◽  
Yield Model ◽  
Pinus Roxburghii ◽  
Phenotypic Data ◽  
Gene Effects ◽  
Model Based Clustering ◽  
Model Based ◽  
Nuclear Ssr

Abstract Association mapping is a method for detection of gene effects based on linkage disequilibrium (LD) that complements QTL analysis in the development of tools for molecular plant breeding. A total of 240 genotypes of Pinus roxburghii (Himalayan Chir Pine) from a natural population in Chakrata division (Tiunee range), Uttarakhand (India) were evaluated for resin yield. Based on the phenotypic data and stable resin production in consecutive years, 53 genotypes were selected after excluding the individuals with similar resin production. The selected 53 individuals were best representatives of the variation in resin yield in Chakrata population which varied between 0.25 and 8.0 kg/tree/year and were used for genotyping and association analysis using SSR markers. Out of 80 primers initially screened, a total of 19 polymorphic SSRs (11 cpSSR and 8 nSSR) were used in the study. Model based clustering using 19 polymorphic SSR markers identified five subpopulations among these genotypes. LD was evaluated using the entire population. The squared allele frequency correlation, r2 was estimated for each pair of SSR loci. The comparison wise significance (p-values) of SSR marker pairs was determined by performing 100,000 permutations. The genetic divergence ranged from 50 to 100%. The UPGMA based hierarchial clustering grouped the genotypes in accordance with their resin yield. Model based clustering suggested the existence of five subpopulations in the sample. However, the distribution of P. roxburghii genotypes into five subpopulations had no correlation with their resin yield thus ruling out the possibility of any ancestral relationship among the genotypes with similar resin yield. AMOVA suggested that the variation among P. roxburghii genotypes at the molecular level was related with the variation in resin yield and not their site of collection thus highlighting the genetic basis of the trait. LD based association analysis revealed two chloroplast SSRs Pt71936 and Pt87268 and one nuclear SSR pm09a to be in significant association with resin yield. The two associated chloroplast SSRs showed significant LD (p<0.01). One of the chloroplast SSR Pt87268 showing association with resin yield was also found to be in significant LD with the nuclear SSR pm07, further showing the probability of this marker also to be associated with resin yield.

A Productivity-Oriented Wafer Map Optimization Using Yield Model Based on Machine Learning

2019 ◽  
Vol 32 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Jong-Seong Kim ◽  
Sung-Ju Jang ◽  
Tae-Woo Kim ◽  
Hyun-Jin Lee ◽  
Jong-Bae Lee
Keyword(s):  
Machine Learning ◽  
Yield Model ◽  
Model Based
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