Using deep learning approaches to overcome limited dataset issues within semiconductor domain

Tamrin, Milad Omrani; Henwood, Sébastien; Dubois, Jean-Francois; Brault, Jean-Jules; Chidami, Saad; Bassetto, Samuel

doi:10.1109/newcas44328.2019.8961246

Cited by 1 publication

(1 citation statement)

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“…Wang et al 22 addressed the imbalance problem in wafer map data by augmenting data via VAE and Ji and Lee 13 demonstrated that in comparison to traditional data augmentation techniques, GAN offered improved data augmentation for the identification of defective wafer maps. Furthermore, Tamrin et al 23 showed that in scenarios with limited data, GAN generated realistic wafer map images, but VAE failed to converge not providing realistic wafer map images.…”

Section: Introductionmentioning

confidence: 99%

Comparison of deep generative models for real-time generation of synthesized defective wafer maps

Alam,

Kehtarnavaz

2024

Real-Time Image Processing and Deep Learning 2024

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Modern wafer inspection systems in Integrated Circuit (IC) manufacturing utilize deep neural networks. The training of such networks requires the availability of a very large number of defective or faulty die patterns on a wafer called wafer maps. The number of defective wafer maps on a production line is often limited. In order to have a very large number of defective wafer maps for the training of deep neural networks, generative models can be utilized to generate realistic synthesized defective wafer maps. This paper compares the following three generative models that are commonly used for generating synthesized images: Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), and CycleGAN which is a variant of GAN. The comparison is carried out based on the public domain wafer map dataset WM-811K. The quality aspect of the generated wafer map images is evaluated by computing the five metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), inception score (IS), Fréchet inception distance (FID), and kernel inception distance (KID). Furthermore, the computational efficiency of these generative networks is examined in terms of their deployment in a real-time inspection system.

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Section: Introductionmentioning

confidence: 99%