TY - JOUR
T1 - Inverse Gaussian distribution based timing analysis of Sub-threshold CMOS circuits
AU - Chen, Jiaoyan
AU - Cotofana, Sorin
AU - Grandhi, Satish
AU - Spagnol, Christian
AU - Popovici, Emanuel
N1 - Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Aggressive technology scaling and ultra low power constraints have resulted in less predictable device behavior complicating timing analysis/estimation. The traditional delay models fail to accurately capture the circuit behavior under such conditions. This paper proposes a novel highly accurate Inverse Gaussian Distribution (IGD) based delay model applicable to both combinational and sequential elements for sub-powered circuits. The IGD based delay estimation accuracy is demonstrated by evaluating multiple circuits, i.e., D Flip Flops (DFFs) + 8-bit Ripple Carry Adder, and 8-bit De-multiplexer (DEMUX) and Multiplexer (MUX). Our experiments indicate that the IGD based approach provides a high matching against HSPICE Monte Carlo simulation results, with an average error less than 1.9% and 1.2% for the two circuits, respectively, while sparing orders of magnitude simulation time. Moreover, the IGD model outperforms the traditional Gaussian Distribution (GD) model by providing 6 × better average accuracy with no extra simulation time overhead.
AB - Aggressive technology scaling and ultra low power constraints have resulted in less predictable device behavior complicating timing analysis/estimation. The traditional delay models fail to accurately capture the circuit behavior under such conditions. This paper proposes a novel highly accurate Inverse Gaussian Distribution (IGD) based delay model applicable to both combinational and sequential elements for sub-powered circuits. The IGD based delay estimation accuracy is demonstrated by evaluating multiple circuits, i.e., D Flip Flops (DFFs) + 8-bit Ripple Carry Adder, and 8-bit De-multiplexer (DEMUX) and Multiplexer (MUX). Our experiments indicate that the IGD based approach provides a high matching against HSPICE Monte Carlo simulation results, with an average error less than 1.9% and 1.2% for the two circuits, respectively, while sparing orders of magnitude simulation time. Moreover, the IGD model outperforms the traditional Gaussian Distribution (GD) model by providing 6 × better average accuracy with no extra simulation time overhead.
KW - CMOS process variations
KW - Delay model
KW - Near/sub-threshold operation
KW - Statistical modeling
KW - Timing analysis
UR - https://www.scopus.com/pages/publications/84948843855
U2 - 10.1016/j.microrel.2015.09.021
DO - 10.1016/j.microrel.2015.09.021
M3 - Article
AN - SCOPUS:84948843855
SN - 0026-2714
VL - 55
SP - 2754
EP - 2761
JO - Microelectronics Reliability
JF - Microelectronics Reliability
IS - 12
ER -