_:n2dc3a6bc540845f1bb519fc70e49d65bxb20
<http://schema.org/description>
"Over the last few decades, modern industrial processes have investigated\nseveral cost-effective methodologies to improve the productivity and yield of\nsemiconductor manufacturing. While playing an essential role in facilitating\nreal-time monitoring and control, the data-driven soft-sensors in industries\nhave provided a competitive edge when augmented with deep learning approaches\nfor wafer fault-diagnostics. Despite the success of deep learning methods\nacross various domains, they tend to suffer from bad performance on\nmulti-variate soft-sensing data domains. To mitigate this, we propose a\nsoft-sensing ConFormer (CONvolutional transFORMER) for wafer fault-diagnostic\nclassification task which primarily consists of multi-head convolution modules\nthat reap the benefits of fast and light-weight operations of convolutions, and\nalso the ability to learn the robust representations through multi-head design\nalike transformers. Another key issue is that traditional learning paradigms\ntend to suffer from low performance on noisy and highly-imbalanced soft-sensing\ndata. To address this, we augment our soft-sensing ConFormer model with a\ncurriculum learning-based loss function, which effectively learns easy samples\nin the early phase of training and difficult ones later. To further demonstrate\nthe utility of our proposed architecture, we performed extensive experiments on\nvarious toolsets of Seagate Technology's wafer manufacturing process which are\nshared openly along with this work. To the best of our knowledge, this is the\nfirst time that curriculum learning-based soft-sensing ConFormer architecture\nhas been proposed for soft-sensing data and our results show strong promise for\nfuture use in soft-sensing research domain."
<https://cdnjs.deepai.org/profile/xiaoye-qian>