The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
CMOS 장치의 소형화로 인해 프로세스 변동이 CMOS 기반 아날로그 회로 오류의 주요 원인이 되고 있습니다. 예를 들어, 피처 크기가 5%만 변해도 회로 오류가 발생할 수 있습니다. 몬테카를로(Monte-Carlo) 및 코너 기반 검증과 같은 다양한 방법은 문제를 포착하기 전에 수천 번의 시뮬레이션을 통해 문제로 인한 변동을 예측하는 데 도움이 됩니다. 본 논문에서는 아날로그 회로 성능 예측을 위한 새로운 방법론을 제시합니다. 새로운 방법은 먼저 회로의 모든 관련 장치에 대한 통계적 불확실성 분석을 적용합니다. 매개변수 가변성의 불확실성 중요성을 평가함으로써 결과 출력에 가장 중요한 구성 요소만으로 회로를 근사화합니다. 결과 시스템에 CAA(Chebyshev Affine Arithmetic)를 적용하면 시간 영역과 주파수 영역에서 성능 범위와 확률 정보가 모두 제공됩니다.
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Jin SUN, Kiran POTLURI, Janet M. WANG, "Predicting Analog Circuit Performance Based on Importance of Uncertainties" in IEICE TRANSACTIONS on Electronics,
vol. E93-C, no. 6, pp. 893-904, June 2010, doi: 10.1587/transele.E93.C.893.
Abstract: With the scaling down of CMOS devices, process variation is becoming the leading cause of CMOS based analog circuit failures. For example, a mere 5% variation in feature size can trigger circuit failure. Various methods such as Monte-Carlo and corner-based verification help predict variation caused problems at the expense of thousands of simulations before capturing the problem. This paper presents a new methodology for analog circuit performance prediction. The new method first applies statistical uncertainty analysis on all associated devices in the circuit. By evaluating the uncertainty importance of parameter variability, it approximates the circuit with only components that are most critical to output results. Applying Chebyshev Affine Arithmetic (CAA) on the resulting system provides both performance bounds and probability information in time domain and frequency domain.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.E93.C.893/_p
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@ARTICLE{e93-c_6_893,
author={Jin SUN, Kiran POTLURI, Janet M. WANG, },
journal={IEICE TRANSACTIONS on Electronics},
title={Predicting Analog Circuit Performance Based on Importance of Uncertainties},
year={2010},
volume={E93-C},
number={6},
pages={893-904},
abstract={With the scaling down of CMOS devices, process variation is becoming the leading cause of CMOS based analog circuit failures. For example, a mere 5% variation in feature size can trigger circuit failure. Various methods such as Monte-Carlo and corner-based verification help predict variation caused problems at the expense of thousands of simulations before capturing the problem. This paper presents a new methodology for analog circuit performance prediction. The new method first applies statistical uncertainty analysis on all associated devices in the circuit. By evaluating the uncertainty importance of parameter variability, it approximates the circuit with only components that are most critical to output results. Applying Chebyshev Affine Arithmetic (CAA) on the resulting system provides both performance bounds and probability information in time domain and frequency domain.},
keywords={},
doi={10.1587/transele.E93.C.893},
ISSN={1745-1353},
month={June},}
부
TY - JOUR
TI - Predicting Analog Circuit Performance Based on Importance of Uncertainties
T2 - IEICE TRANSACTIONS on Electronics
SP - 893
EP - 904
AU - Jin SUN
AU - Kiran POTLURI
AU - Janet M. WANG
PY - 2010
DO - 10.1587/transele.E93.C.893
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E93-C
IS - 6
JA - IEICE TRANSACTIONS on Electronics
Y1 - June 2010
AB - With the scaling down of CMOS devices, process variation is becoming the leading cause of CMOS based analog circuit failures. For example, a mere 5% variation in feature size can trigger circuit failure. Various methods such as Monte-Carlo and corner-based verification help predict variation caused problems at the expense of thousands of simulations before capturing the problem. This paper presents a new methodology for analog circuit performance prediction. The new method first applies statistical uncertainty analysis on all associated devices in the circuit. By evaluating the uncertainty importance of parameter variability, it approximates the circuit with only components that are most critical to output results. Applying Chebyshev Affine Arithmetic (CAA) on the resulting system provides both performance bounds and probability information in time domain and frequency domain.
ER -