Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review

Abstract

With the high demand and sub-nanometer design for integrated circuits, surface defect complexity and frequency for semiconductor wafers have increased; subsequently emphasizing the need for highly accurate fault detection and root-cause analysis systems as manual defect diagnosis is more time-intensive, and expensive. As such, machine learning and deep learning methods have been integrated to automated inspection systems for wafer map defect recognition and classification to enhance performance, overall yield, and cost-efficiency. Concurrent with algorithm and hardware advances, in particular the onset of neural networks like the convolutional neural network, the literature for wafer map defect detection exploded with new developments to address the limitations of data preprocessing, feature representation and extraction, and model learning strategies. This article aims to provide a comprehensive review on the advancement of machine learning and deep learning applications for wafer map defect recognition and classification. The defect recognition and classification methods are introduced and analyzed for discussion on their respective advantages, limitations, and scalability. The future challenges and trends of wafer map detection research are also presented.

Appendix

Abbreviation	Term
AC	Adjacency Clustering
AdaBalGAN	Adaptive Balancing Generative Adversarial Network
AMI	Adjusted Mutual Information
ANN	Artificial Neural Network
ARI	Adjusted Rand Index
BALD	Bayesian Active Learning by Disagreement
BO	Bayesian Optimization
BB	Bounding Box
BPN	Back Propagation Network
C2DPCA	Conditional Two-Dimensional PCA
CCD	Charge-Coupled Devices
CMP	Chemical Mechanical Process
CNN	Convolutional Neural Network
CPF	Connected-Path Filtering
CVAE	Convolutional Variational Autoencoder
CZ	Czochralski
DBN	Deep Belief Network
DBSCAN	Density-based Spatial Clustering of Applications with Noise
DCN	Deformable Convolutional Network
DCNN	Deep Convolutional Neural Network
DDPfinder	Dominant Defective Patterns Finder
DFS	Depth-first Search
DL	Deep Learning
DP	Dirichlet Process
ECOC	Error-Correcting Output Codes
EMR	Exact Match Ratio
ESDAE	Enhanced Stacked Denoising Autoencoder
EUV	Extreme Ultraviolet
FAM	Fuzzy ARTMAP
FD	Fischer-discriminant
FZ	Float-zone
GAN	Generative Adversarial Network
GBM	Gradient Boosting Machine
GMM	Gaussian Mixture Model
GRN	Generalized Regression Network
IC	Integrated Circuit
IL	Incremental Learning
ILT	Inverse-lithography Technology
iWMM	Infinite Warped Mixture Model
JLNLDA	Joint Local and Non-local Linear Discriminant Analysis
kNN	k-Nearest Neighbors
LDA	Linear Discriminant Analysis
LLE	Locally Linear Embedding
LR	Logistic Regression
MDS	Multi-Dimensional Scaling
MI	Mutual Information
ML	Machine Learning
MLP	Multi-Layer Perceptron
MPre	Micro-Precision
MRe	Micro-Recall
NMI	Normalized Mutual Information
OPTICS	Ordering Point to Identify the Cluster Structure
PCA	Principal Component Analysis
PCACAE	PCA-based Convolutional Autoencoder
RCA	Root-cause Analysis
RF	Random Forest
RGRN	Randomized General Regression Network
RI	Rand Index
RL	Reinforcement Learning
RNN	Recurrent Neural Network
SAT	Scanning Acoustic Tomography
SCSDAE	Stacked Convolutional Sparse Denoising Autoencoder
SDAE	Stacked Denoising Autoencoder
SEM	Scanning Electron Microscopy
SS-CDGMM	Semi-supervised Convolutional Deep Generative Multiple Models
SSD	Single Shot Detector
SVAE	Semi-supervised Variational Autoencoder
SVC	Support Vector Clustering
SVE	Soft Voting Ensemble
SVM	Support Vector Machine
t-SNE	t-distributed Stochastic Neighbor Embedding
TTV	Total Thickness Variation
UV	Ultraviolet
VAE	Variational Autoencoder
WBM	Wafer Bin Map
WM	Wafer Map
WMDD	Wafer Map Defect Detection