Nonlinear Variation Decomposition of Neural Networks for Holistic Semiconductor Process Monitoring

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-09-04 DOI:10.1002/aisy.202300920
Hyeok Yun, Hyundong Jang, Seunghwan Lee, Junjong Lee, Kyeongrae Cho, Seungjoon Eom, Soomin Kim, Choong-Ki Kim, Hong-Chul Byun, Seongjoo Han, Min-Soo Yoo, Rock-Hyun Baek
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Abstract

Artificial intelligence (AI) is increasingly used to solve multi-objective problems and reduce the turnaround times of semiconductor processes. However, only brief AI explanations are available for process/device/circuit engineers to provide holistic feedback on the manufactured results. Herein, linear/nonlinear variation decomposition (LVD/NLVD) of neural networks is demonstrated to quantitatively evaluate the influence of unit processes on the figure of merit (FoM) and co-analyze the unit process influences with device characteristic behaviors. The NLVD can evaluate the output variation from each input of neural networks in an individual sample, although neural networks are not available in an analytic form. The NLVD is successfully verified by confirming that a) the output and summation of all decomposed output variations perfectly coincide and b) the process influences on the FoM are decomposed to 6.01–54.86% more accurately compared with those of LVD in 1Y nm node dynamic random-access memory test vehicles with a baseline and split tests introducing high-k metal gates with a minimum gate length of 1 A nm node for further node scaling. The approaches identify defective processes and defect mechanisms in each sample and wafer, which enhance causal analyses for individual cases in diverse fields based on regression artificial neural networks.

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用于整体半导体过程监控的神经网络非线性变化分解
人工智能(AI)越来越多地被用于解决多目标问题和缩短半导体工艺的周转时间。然而,工艺/器件/电路工程师只能获得简短的人工智能解释,以便对制造结果提供整体反馈。在此,我们展示了神经网络的线性/非线性变化分解(LVD/NLVD),以定量评估单元制程对功绩值(FoM)的影响,并共同分析单元制程影响与器件特征行为。尽管神经网络没有分析形式,但 NLVD 可以评估单个样本中神经网络每个输入的输出变化。NLVD 成功地验证了:a) 所有分解输出变化的输出和总和完全重合;b) 在 1Y nm 节点动态随机存取存储器测试车辆中,与 LVD 相比,工艺对 FoM 的影响分解精确度提高了 6.01-54.86%。这些方法可识别每个样品和晶圆中的缺陷过程和缺陷机制,从而加强基于回归人工神经网络的不同领域个案的因果分析。
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0.00%
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4 weeks
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