Title
In-situ characterization and extraction of SRAM variability
Abstract
Measurement and extraction of as fabricated SRAM cell variability is essential to process improvement and robust design. This is challenging in practice, due to the complexity in the test procedure and requisite numerical analysis. This work proposes a new singleended test procedure for SRAM cell write margin measurement. Moreover, an efficient decomposition method is developed to extract transistor threshold voltage (VTH) variations from the measurements, allowing accurate determination of SRAM cell stability. The entire approach is demonstrated in a 90nm test chip with 32K cells. The advantages of the proposed method include: (1) a single-ended SRAM test structure with no disturbance to SRAM operations; (2) a convenient test procedure that only requires quasistatic control of external voltages; and (3) a non-iterative method that extracts the VTH variation of each transistor from eight measurements. The new procedure enables accurate predictions of SRAM performance variability. As validated with 90nm data of write margin and data retention voltage, the prediction error from extracted VTH variations is
Year
DOI
Venue
2010
10.1145/1837274.1837454
DAC
Keywords
Field
DocType
sram performance variability,new singleended test procedure,sram operation,sram cell,in-situ characterization,test chip,convenient test procedure,sram cell variability,sram cell stability,vth variation,single-ended sram test structure,sram variability,iteration method,chip,decomposition method,transistors,numerical analysis,extraction,computational complexity,threshold voltage,switches,silicon,production,fluctuations,data mining,prediction error
Computer science,Voltage,Static random-access memory,Electronic engineering,Chip,Real-time computing,Numerical analysis,Transistor,Data retention voltage,Threshold voltage,Computational complexity theory
Conference
ISSN
ISBN
Citations 
0738-100X
978-1-4244-6677-1
1
PageRank 
References 
Authors
0.45
6
8
Name
Order
Citations
PageRank
Srivatsan Chellappa1223.45
Jia Ni210.45
Xiaoyin Yao351.60
Nathan D. Hindman420.84
Jyothi Velamala5534.83
Min Chen6626.46
Yu Cao72765245.91
Lawrence T. Clark815533.27