Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection

TL;DR

This paper presents a deep learning approach to generate synthetic golden standards for InP wafer defect detection, enabling more efficient and automated quality control in semiconductor manufacturing without relying on real defect examples.

Contribution

It introduces a novel deep neural network method to simulate photo-realistic wafer images from CAD data, facilitating unsupervised defect detection in multi-project wafers.

Findings

Deep learning outperforms baseline decision-tree methods.

Synthetic images enable effective defect detection.

Method applicable to real wafer photographs.

Abstract

Quality management in semiconductor manufacturing often relies on template matching with known golden standards. For Indium-Phosphide (InP) multi-project wafer manufacturing, low production scale and high design variability lead to such golden standards being typically unavailable. Defect detection, in turn, is manual and labor-intensive. This work addresses this challenge by proposing a methodology to generate a synthetic golden standard using Deep Neural Networks, trained to simulate photo-realistic InP wafer images from CAD data. We evaluate various training objectives and assess the quality of the simulated images on both synthetic data and InP wafer photographs. Our deep-learning-based method outperforms a baseline decision-tree-based approach, enabling the use of a 'simulated golden die' from CAD plans in any user-defined region of a wafer for more efficient defect detection. We apply our method to a template matching procedure, to demonstrate its practical utility in surface defect detection.