Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits

Kuo, Chin-Cheng; Hu, Wei-Yi; Chen, Yi‐Hung; Kuan, J. F.; Cheng, Yi-Kan

doi:10.1145/2228360.2228563

Cited by 8 publications

(1 citation statement)

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“…This small variation is 1σ of each distribution nominal value. After obtaining the effect of each variation on the performance parameters, a linear model estimates roughly the output distance of each sample performance from the minimum desired one; consequently, the design points whose performance do not meet the minimum requirement cannot pass this phase . Over the next phase, MC simulation is run just on the practical solutions in each generation, and impractical solutions do not emerge between the final results.…”

Section: Main Structure Of the Proposed Algorithmmentioning

confidence: 99%

Yield‐aware sizing of pipeline ADC using a multiple‐objective evolutionary algorithm

Mirzaie

Shamsi

Byun

2016

Circuit Theory & Apps

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Summary In this paper, an efficient and highly accurate algorithm for yield‐aware design of pipeline analog to digital converters (ADCs) based on a four‐step approach is presented. First, a general netlist is generated for the data converter building blocks. Then, using an evolutionary method, its best performance sizing is estimated. Finally, the yield gets enhanced to reach a desired value. With the same accuracy, the presented algorithm can achieve yield optimization by approximately five times less computational cost compared with a state‐of‐the‐art MC‐based method. The framework is applied to demonstrate a reliable converter with optimum performance, power consumption, speed and area overhead during a single running process. A prototype 10‐bit resolution, 10‐MS/s pipeline analog to digital converter has been simulated in a 0.18‐µm 1.8‐V CMOS process. Presented results, applying the proposed method, show important advantages in terms of accuracy and efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Main Structure Of the Proposed Algorithmmentioning

confidence: 99%