A certain amount of process deviations characterizes semiconductor manufacturing processes. Automated detection of these production issues followed by an automated root cause analysis has the potential to increase the effectiveness of semiconductor production. Manufacturing defects exhibit typical patterns in measured wafer test data, e.g., rings, spots, repetitive textures, or scratches. Recognizing these patterns is an essential step for finding the root cause of production issues. This paper demonstrates that combining Information Maximizing Generative Adversarial Network (InfoGAN) and Wasserstein GAN (WGAN) with a new loss function is suitable for extracting the most characteristic features from extensive real-world sensory wafer test data, which in various aspects outperforms traditional unsupervised techniques. These features are then used in subsequent clustering tasks to group wafers into clusters according to their exhibit patterns. The primary outcome of this work is a statistical generative model for recognizing spatial wafermaps patterns using deep adversarial neural networks. We experimentally evaluate the performance of the proposed approach over a real dataset.

Semiconductor manufacturing processes are prone to process deviations or other production issues. Quality assurance of every processing step and measuring wafer test values is crucial for finding possible root causes of these problems. Automated visual inspection and recognition of patterns in wafermap data obtained during different processing steps has a potential to signifficantly improve the efficiency of finding early production issues and even help with adjustment of the production parameters to automatically resolve them. In this paper, we present a machine learning approach for unsupervised clustering of spatial patterns in wafermap measurement data. Measured test values are first pre-processed using some computer vision techniques, followed by a feature extraction based on variational autoencoders to decompose high-dimensional wafermaps into a low-dimensional latent representation. Final step is to detect the structure of this latent space and assign individual wafers into clusters. We experimentally evaluate the performance of the proposed method over a real dataset.

Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., information diffusion, virus propagation) occurring over the unobserved network. While there have been efforts to infer networks based on such data, providing a generative probabilistic model that is able to identify the underlying time-varying network remains an open question. Here we consider the problem of inferring generative dynamic network models based on network cascade diffusion data. We propose a novel framework for providing a non-parametric dynamic network model---based on a mixture of coupled hierarchical Dirichlet processes---based on data capturing cascade node infection times. Our approach allows us to infer the evolving community structure in networks and to obtain an explicit predictive distribution over the edges of the underlying network---including those that were not involved in transmission of any cascade, or are likely to appear in the future. We show the effectiveness of our approach using extensive experiments on synthetic as well as real-world networks.

One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and illustrate that the proposed method detects the inter-community links accurately even with low number of measurements (i.e. when the number of measurements is less than half of the number of existing links in the network).