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Convolution/deconvolution SRAM analyses for complex gamma mixtures RTN distributions

机译:卷积/解卷积SRAM分析,用于复杂的伽玛混合物RTN分布

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This paper discusses, for the first time, how the statistical SRAM design analyses should be changed when: (1) the time-dependent (TD) voltage margin variations (MV) after the screening test will dominate the overall behavior of the MV variations and (2) the shapes of the MV distribution will change from the Gaussian to the complex mixtures of Gamma distributions. We discuss on the SRAM TD-MV analyses with not only the forward problem but also the inverse problem, i.e., deconvolution analyses. The proposed algorithm for the deconvolution to circumvent the issues caused by a high-pass filtering behavior is discussed. The results given by the proposed convolution /deconvolution design analyses have shown that the followings have been enabled: (1) to figure out the unknown truncating point (TP) based on the overall final MV distributions as a reverse-engineering, (2) to decide the aimed TP/RDF/RTN required to achieve a target specs, and (3) to work out the required MV-shift-amount by the assisted circuit schemes to avoid any out of specs in the market during the life-time.
机译:本文首次讨论了在以下情况下应如何更改统计SRAM设计分析:(1)筛选测试后随时间变化的(TD)电压裕量变化(MV)将主导MV变化的总体行为,以及(2)MV分布的形状将从高斯变为伽玛分布的复杂混合。我们讨论SRAM TD-MV分析时不仅要考虑正向问题,还要考虑反问题,即反卷积分析。讨论了所提出的反卷积算法,以解决由高通滤波行为引起的问题。所提出的卷积/解卷积设计分析给出的结果表明,已启用以下功能:(1)基于总体最终MV分布,将未知的截断点(TP)进行逆向工程;(2)确定达到目标规格所需的目标TP / RDF / RTN,以及(3)通过辅助电路方案算出所需的MV偏移量,以避免在整个生命周期内出现任何超出规格的市场。

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