### Uittreksel

Taal | Engels |
---|---|

Artikelnummer | 8:11 |

Aantal pagina's | 23 |

Tijdschrift | Journal of Mathematics in Industry |

Volume | 8 |

Nummer van het tijdschrift | 1 |

DOI's | |

Status | Gepubliceerd - 25 okt 2018 |

### Vingerafdruk

### Trefwoorden

### Citeer dit

*Journal of Mathematics in Industry*,

*8*(1), [8:11]. DOI: 10.1186/s13362-018-0053-4

}

*Journal of Mathematics in Industry*, vol. 8, nr. 1, 8:11. DOI: 10.1186/s13362-018-0053-4

**Hybrid importance sampling Monte Carlo approach for yield estimation in circuit design.** / Tyagi, A.K.; Jonsson, Xavier; Beelen, Theo G.J.; Schilders, Wil H.A.

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

TY - JOUR

T1 - Hybrid importance sampling Monte Carlo approach for yield estimation in circuit design

AU - Tyagi,A.K.

AU - Jonsson,Xavier

AU - Beelen,Theo G.J.

AU - Schilders,Wil H.A.

PY - 2018/10/25

Y1 - 2018/10/25

N2 - The dimension of transistors shrinks with each new technology developed in the semiconductor industry. The extreme scaling of transistors introduces important statistical variations in their process parameters. A large digital integrated circuit consists of a very large number (in millions or billions) of transistors, and therefore the number of statistical parameters may become very large if mismatch variations are modeled. The parametric variations often cause to the circuit performance degradation. Such degradation can lead to a circuit failure that directly affects the yield of the producing company and its fame for reliable products. As a consequence, the failure probability of a circuit must be estimated accurately enough. In this paper, we consider the Importance Sampling Monte Carlo method as a reference probability estimator for estimating tail probabilities. We propose a Hybrid ISMC approach for dealing with circuits having a large number of input parameters and provide a fast estimation of the probability. In the Hybrid approach, we replace the expensive to use circuit model by its cheap surrogate for most of the simulations. The expensive circuit model is used only for getting the training sets (to fit the surrogates) and near to the failure threshold for reducing the bias introduced by the replacement.

AB - The dimension of transistors shrinks with each new technology developed in the semiconductor industry. The extreme scaling of transistors introduces important statistical variations in their process parameters. A large digital integrated circuit consists of a very large number (in millions or billions) of transistors, and therefore the number of statistical parameters may become very large if mismatch variations are modeled. The parametric variations often cause to the circuit performance degradation. Such degradation can lead to a circuit failure that directly affects the yield of the producing company and its fame for reliable products. As a consequence, the failure probability of a circuit must be estimated accurately enough. In this paper, we consider the Importance Sampling Monte Carlo method as a reference probability estimator for estimating tail probabilities. We propose a Hybrid ISMC approach for dealing with circuits having a large number of input parameters and provide a fast estimation of the probability. In the Hybrid approach, we replace the expensive to use circuit model by its cheap surrogate for most of the simulations. The expensive circuit model is used only for getting the training sets (to fit the surrogates) and near to the failure threshold for reducing the bias introduced by the replacement.

KW - Yield

KW - Failure probability

KW - Monte Carlo

KW - Hybrid importance sampling Monte Carlo

KW - Dimension reduction

KW - Exploration phase

KW - Estimation Phase

KW - Kriging model

KW - Probability estimator

U2 - 10.1186/s13362-018-0053-4

DO - 10.1186/s13362-018-0053-4

M3 - Article

VL - 8

JO - Journal of Mathematics in Industry

T2 - Journal of Mathematics in Industry

JF - Journal of Mathematics in Industry

SN - 2190-5983

IS - 1

M1 - 8:11

ER -