Paper
4 March 2008 VARAN: variability analysis for memory cell robustness
Gideon Reisfeld, Dmitry Messerman, Nir Bone, Adi Lazar
Author Affiliations +
Abstract
Increasing memory array sizes and low operating voltages in modern ICs expose the IC to extremely low failure rates of single memory cells. The failure probability is affected by variations in the process of IC fabrication, which yield varying transistor parameters. This may cause an erratic certification of designs that may have a low production yield. VARAN relies on analytical methods that yield controlled precision calculations involving a minimum of circuit simulation. Furthermore, VARAN is equipped with a built-in sensitivity analysis mechanism that can guide the designer as to which parameters are significant for robust design. The flow starts by setting a circuit simulation infrastructure. Each simulation returns a value of fail or pass for a given set of circuit parameters (i.e. transistor size) and environmental parameters. The probability of failure is calculated by integration over the design parameter space. VARAN uses a novel response surface modeling (RSM) to reduce the number of simulations that are needed for low probability calculations. The RSM relies on an adaptive fitting, which is capable of modeling intricate behaviors which require many parameters using ordinary polynomial modeling, with a minimal number of terms. VARAN was tested over synthetic and real circuit data yielding extremely low failure probabilities.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gideon Reisfeld, Dmitry Messerman, Nir Bone, and Adi Lazar "VARAN: variability analysis for memory cell robustness", Proc. SPIE 6925, Design for Manufacturability through Design-Process Integration II, 69250L (4 March 2008); https://doi.org/10.1117/12.777269
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Failure analysis

Device simulation

Computer aided design

Computer simulations

Transistors

Binary data

Data modeling

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