scholarly journals Research on Construction of Teaching Resource Library Base of IC Manufacturing Process

2018 ◽  
Author(s):  
Li Shu-ping
Keyword(s):  
Manufacturing Process ◽  
Teaching Resource ◽  
Ic Manufacturing ◽  
Resource Library

Development and Application of Information Technology in University Basketball Network Courses

2014 ◽  
Vol 1078 ◽  
pp. 345-348
Author(s):  
Ke Fei Wang
Keyword(s):  
Computer Network ◽  
Sports Injuries ◽  
Theory And Practice ◽  
Online Forums ◽  
Network Teaching ◽  
Module Design ◽  
Teaching Resource ◽  
System Maintenance ◽  
Network Platform ◽  
Resource Library

Based on the university basketball teaching theory and practice,combined with module design theory.Using literature studies,surveys,interviews,computer network design and software development,testing feedback methods designed modern colleges.basketball network teaching courses.The system includes basketball theoretical teaching resource library,basketball skills teaching resource library, basketball learning online forums, basketball sports injuries advisory system and system maintenance updates five modules. Expounded the issue of the system design, key technologies, modules and functional design,running instances and so on. Research and development of the system provided a good network platform for the college basketball training,so make the existing basketball teaching and training network, made the quality of teaching to improve and enhance.

Analysis of IC Manufacturing Process Deformations: An Automated Approach Using SRAM Bit Fail Maps

2003 ◽  
Author(s):  
T. Zanon ◽  
M. Ferdman ◽  
W. Maly ◽  
K. Komeyli
Keyword(s):  
Test Data ◽  
Manufacturing Process ◽  
Rich Source ◽  
Analysis Framework ◽  
Test Vehicle ◽  
Ic Manufacturing ◽  
Source Of Information

Abstract SRAM bit fail maps (BFM) are routinely collected during earlier phases of yield ramping, providing a rich source of information for IC failure and deformation learning. In this paper, we present an automated approach to analyzing BFM data efficiently. We also demonstrate the usability of our analysis framework using real BFM test data from a large, modern SRAM test vehicle.

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