本文選題：靜態(tài)隨機(jī)存儲(chǔ)器 + 深亞微米��； 參考：《復(fù)旦學(xué)報(bào)(自然科學(xué)版)》2014年03期

【摘要】：隨著納米工藝的不斷推進(jìn),由隨機(jī)工藝參數(shù)偏差引起的電路穩(wěn)定性問題日益突出.靜態(tài)存儲(chǔ)器由大規(guī)模高度重復(fù)性電路單元結(jié)構(gòu)組成,要保證整個(gè)芯片工作正常,要求單個(gè)單元電路失效的概率極低,相關(guān)的失效事件屬于極端概率事件.傳統(tǒng)的蒙特卡洛采樣在產(chǎn)生足夠的有效采樣點(diǎn)和精確捕捉實(shí)際失效區(qū)域分布方面存在諸多缺陷.本文采用的自適應(yīng)增強(qiáng)(AdaBoost)方法是一種新的統(tǒng)計(jì)學(xué)習(xí)技術(shù),通過樣本學(xué)習(xí)構(gòu)建一個(gè)強(qiáng)分類器,可以過濾掉大量無(wú)效的采樣點(diǎn),使采樣點(diǎn)更集中的分布在失效區(qū)域中.另外,本文引入交叉熵方法改進(jìn)傳統(tǒng)的重要性采樣算法,達(dá)到更快的收斂速度.該方法綜合了數(shù)據(jù)挖掘和極值理論的思想,能夠快速精確地捕捉失效區(qū)域?qū)嶋H分布,相比于標(biāo)準(zhǔn)的蒙特卡洛采樣方法,在不損失精度的情況下效率提高了1000倍以上.
[Abstract]:With the continuous advancement of nanotechnology, the problem of circuit stability caused by random process parameter deviation has become increasingly prominent. Static memory is composed of large and highly repetitive circuit unit structure. To ensure that the whole chip is working normally, the probability of failure of single unit circuit is very low, and the related failure events belong to extreme probability events. The traditional Monte Carlo sampling has many defects in producing enough effective sampling points and accurately capturing the distribution of actual failure regions. The adaptive enhancement (AdaBoost) method used in this paper is a new statistical learning technique. A strong classifier is constructed through sample learning, which can filter out a large number of invalid sampling points and make the sampling point. More concentrated in the failure area. In addition, this paper introduces the cross entropy method to improve the traditional importance sampling algorithm to achieve faster convergence speed. This method combines the idea of data mining and extremum theory, and can quickly and accurately capture the actual distribution of the failure region, compared to the standard Monte Carlo sampling method, it is not lost. In the case of precision, the efficiency is increased by more than 1000 times.
【作者單位】： 復(fù)旦大學(xué)專用集成電路與系統(tǒng)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(2011CB309701) 國(guó)家自然科學(xué)基金(61106032、91330201、61125401) 國(guó)家十二五科技重大專項(xiàng)項(xiàng)目(2014ZX02301001-005-002) 上海市領(lǐng)軍人才資助項(xiàng)目
【分類號(hào)】：TP333

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