论文标题
使用收缩深度特征和整体熵降低的最大化估计高维产量估计
High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction
论文作者
论文摘要
尽管在过去的十年中，在机器学习技术的帮助下，高速度产量分析的进步很快，但主要的挑战之一是维度的诅咒，这是不可避免的，在处理现代的大规模电路时，仍未解决。为了解决这一挑战，我们提出了一个绝对的收缩深内核学习，ASDK自动识别非线性与深层内核中的主要过程变化参数，并充当模拟昂贵的Spice Migulation的替代模型。为了进一步提高产量估计效率，我们提出了对有效模型更新的近似熵降低的新颖最大化，这也可以通过并行批次采样进行并行计算增强，从而使其可以进行实际部署。 SRAM柱电路上的实验证明了ASDK优于最先进的方法（SOTA）方法，其准确性和效率高达10.3倍，超过SOTA方法。
Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of dimensionality, which is inevitable when dealing with modern large-scale circuits, remains unsolved. To resolve this challenge, we propose an absolute shrinkage deep kernel learning, ASDK, which automatically identifies the dominant process variation parameters in a nonlinear-correlated deep kernel and acts as a surrogate model to emulate the expensive SPICE simulation. To further improve the yield estimation efficiency, we propose a novel maximization of approximated entropy reduction for an efficient model update, which is also enhanced with parallel batch sampling for parallel computing, making it ready for practical deployment. Experiments on SRAM column circuits demonstrate the superiority of ASDK over the state-of-the-art (SOTA) approaches in terms of accuracy and efficiency with up to 10.3x speedup over SOTA methods.
