Image Generation of Ultra-Thin Polymer Films Using Diffusion Models from Tensile Testing for Mechanical Failure Prediction

S. Suh, Haebom Lee, Tae Yeob Kang
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Abstract

The mechanical reliability of ultra-thin polymer films, crucial in microelectronic applications, poses significant challenges. Polymethyl methacrylate (PMMA) is widely utilized in this context, yet conventional methods assessing mechanical reliability are limited in scope, often failing to predict failure accurately. Furthermore, these methods neglect the complex nanoscale physics of polymer films, which may lead to the formation of wrinkles during plastic deformation. In this paper, we present a novel approach that combines diffusion models with measured tensile properties to generate sequential PMMA images, enabling the prediction of mechanical failure in ultra-thin films. The proposed diffusion model not only accurately generates PMMA images from tensile testing but also simulates wrinkle formation, providing a more comprehensive and accurate assessment of PMMA thin film integrity. This research offers a promising avenue for enhancing the mechanical reliability of microelectronic devices.
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利用拉伸测试的扩散模型生成超薄聚合物薄膜的图像,用于机械故障预测
超薄聚合物薄膜在微电子应用中至关重要,其机械可靠性是一项重大挑战。聚甲基丙烯酸甲酯(PMMA)在这方面应用广泛,但评估机械可靠性的传统方法范围有限,往往无法准确预测故障。此外,这些方法还忽视了聚合物薄膜复杂的纳米级物理特性,这可能会导致在塑性变形过程中形成皱纹。在本文中,我们提出了一种新方法,将扩散模型与测量的拉伸特性相结合,生成连续的 PMMA 图像,从而预测超薄薄膜的机械故障。所提出的扩散模型不仅能通过拉伸测试准确生成 PMMA 图像,还能模拟皱纹的形成,从而更全面、准确地评估 PMMA 薄膜的完整性。这项研究为提高微电子器件的机械可靠性提供了一条前景广阔的途径。
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