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- W4311752078 abstract "SRAM yield analysis is critical to the robust SRAM design. However, it is a quite difficult to estimate the SRAM yield because the circuit failure is a “rare-event”. Existing methods are still not efficient enough to solve the problem, especially in high dimensional circuit scenario. In this paper, we present an upgraded version of our conference work, scaled-sigma adaptive importance sampling (SSAIS), improved by adapting projection pursuit regression (PPR). The SSAIS updates not only the location parameters but the scale parameters by searching failure region iteratively. To further reduce the cost of the estimation, we construct PPR model to replace the expensive transistor-level simulation. The model and modeling procedure are integrated into SSAIS successfully by the re-simulation technique. Our method was first validated on SMIC 40 nm SRAM cell and the result outperforms over 2534X than Monte Carlo method and is 3.2X∼7.3X faster than the state-of-art methods with enough accuracy. The comparisons on sense amplifier show our method achieves 1811X speedup over the Monte Carlo method and 2X∼11X speedup over the other methods." @default.
- W4311752078 created "2022-12-28" @default.
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- W4311752078 date "2023-03-01" @default.
- W4311752078 modified "2023-10-18" @default.
- W4311752078 title "An efficient SRAM yield analysis method based on scaled-sigma adaptive importance sampling with meta-model accelerated" @default.
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- W4311752078 doi "https://doi.org/10.1016/j.vlsi.2022.11.015" @default.
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