Effects of defect propagation/growth on in-line defect-based yield prediction

1998 ◽  
Vol 11 (4) ◽  
pp. 546-551 ◽  
Author(s):  
W. Shindo ◽  
R.K. Nurani ◽  
A.J. Strojwas
Keyword(s):  
Yield Prediction ◽  
Line Defect

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Stand-yield prediction for managed Ocala sand pine

1997 ◽  
Author(s):  
D.L. Rockwood ◽  
B. Yang ◽  
K.W. Outcalt
Keyword(s):  
Yield Prediction ◽  
Stand Yield ◽  
Sand Pine

In-Line Defect to Bitmap Signature Correlation: A Shortcut to Physical FA Results

2000 ◽  
Author(s):  
Julie Segal ◽  
Arman Sagatelian ◽  
Bob Hodgkins ◽  
Tom Ho ◽  
Ben Chu ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Integrated Circuit ◽  
Failure Mechanisms ◽  
Memory Devices ◽  
Line Defect ◽  
Yield Improvement ◽  
Electrical Test ◽  
Large Numbers ◽  
Improvement Process ◽  
Existing Data

Abstract Physical failure analysis (FA) of integrated circuit devices that fail electrical test is an important part of the yield improvement process. This article describes how the analysis of existing data from arrayed devices can be used to replace physical FA of some electrical test failures, and increase the value of physical FA results. The discussion is limited to pre-repair results. The key is to use classified bitmaps and determine which signature classification correlates to which type of in-line defect. Using this technique, physical failure mechanisms can be determined for large numbers of failures on a scale that would be unfeasible with de-processing and physical FA. If the bitmaps are classified, two-way correlation can be performed: in-line defect to bitmap failure, as well as bitmap signature to in-line defect. Results also demonstrate the value of analyzing memory devices failures, even those that can be repaired, to gain understanding of defect mechanisms.

Crop Yield Prediction Using Deep Neural Network

2020 ◽  
Author(s):  
Fatin Farhan Haque ◽  
Ahmed Abdelgawad ◽  
Venkata Prasanth Yanambaka ◽  
Kumar Yelamarthi
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Yield Prediction
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