scholarly journals Standard Cell Library Characterization of 28nm Process Based on Machine Learning

Author(s):  
Yi-qi SHE ◽  
Li-jun ZHANG ◽  
Jian-bin ZHENG ◽  
Ai-lin ZHANG ◽  
Yue-ping ZHU ◽  
...  
Author(s):  
Kenza Charafeddine ◽  
Faissal Ouardi

<p>The following work shows an innovative approach to model the timing of<br />standard cells. By using mathematical models instead of the classical SPICE-based characterization, a high amount of CPU (Central Processing Unit) cores is saved and less amount of data is stored. In the present work,<br />characterization of cells of a standard cell library is done in an hour whereas<br />it is done in 650 hours with the classical method. Also, a method for<br />validating and verification of the precision of the modelled data is presented<br />by comparing them on a implemented circuit. The output of implementations shows less than 3% of error between the two methods.</p>


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