A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

A Wafer Bin Map "Relaxed" Clustering Algorithm for Improving Semiconductor Production Yield

AbstractThe semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important since advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing. Production control is often based on the "judgement" of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a network-based data mining approach, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study has been conducted on real production data for validating the proposed clustering algorithm, which showed a perfect correspondence between the malfunction patterns found by the algorithm and those discovered by human experts, so confirming the validity of our approach in its ability of correctly identifying actual defective patterns to help improving production yield

An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification

Silicon wafer defect classification is crucial for improving fabrication and chip production. Although deep learning methods have been successful in single-defect wafer classification, the increasing complexity of the fabrication process has introduced the challenge of multiple defects on wafers, which requires more robust feature learning and classification techniques. Attention mechanisms have been used to enhance feature learning for multiple wafer defects. However, they have limited use in a few mixed-type defect categories, and their performance declines as the number of mixed patterns increases. This work proposes an attention-augmented convolutional neural networks (A2CNN) model for enhanced discriminative feature learning of complex defects. The A2CNN model emphasizes the features in the channel and spatial dimensions. Additionally, the model adopts the focal loss function to reduce misclassification and a global average pooling layer to enhance the network's generalization by reducing overfitting. The A2CNN model is evaluated on the MixedWM38 wafer defect dataset using 10-fold cross-validation. It achieves impressive results, with accuracy, precision, recall, and F1-score reported as 98.66%, 99.0%, 98.55%, and 98.82% respectively. Compared to existing works, the A2CNN model performs better by effectively learning valuable information for complex mixed-type wafer defects

Development Pattern Recognition Model for Classification of Circuit Probe Wafer Maps on Semiconductors

[[abstract]]Circuit probe test is an end of line testing that the individual die has been measured at wafer level in modern semiconductor manufacturing. The test results are visualized as a spatial distribution of the failures on the wafer which can provide some valuable information for the production of failures. In order to reduce time consumption by human operation, a great accuracy of automatic classification system is clear needed for engineering analysis. In this paper, we demonstrate how a robust feature extraction procedure using by classical Hough transform (HT) and circular Hough transform (CHT) can be adapted to detect lines and rounds spatial patterns on circuit probe wafer map. In addition, we also used several technique to detect others spatial patterns. These features which are effectively eliminate the influence of noise to perform pattern classification. The presented methodology is validated with real fabrication data and several data mining classification algorithms are presented to evaluate the advantage of this methodology

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

A High Density Micro-Electrocorticography Device for a Rodent Model

Electrocorticography (ECoG) is a methodology for stable mapping of the brain surface using local field potentials (LFPs) with a wide cortical region, high signal fidelity, and minimal invasiveness to brain tissue. To compare surface ECoG signals with inter-cortical neuronal activity, we fabricated a flexible handcrafted ECoG electrode made with economically available materials. This research is on a Lewis rat implanted with a handcrafted 256-channel, non-penetrative ECoG electrode covering an area of 7mm x 7mm on the cortical surface. This device was placed on the motor and somatosensory cortex of the brain to record signals with an active animal. The recordings are acquired by using the Synapse Software and the Tucker-Davis Technologies acquisition system to monitor and analyze electrophysiological signals within the amplitude range of 200µV for local field potentials. This demonstrates how reactive channels and their spatiotemporal and frequency-specific characteristics can be identified by means of this method

Microfluidic Large Scale Integration and its Application in Image Based Microflow Cytometry

An intelligent image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The development process used stationary cell images as training data. The images are enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracies of various machine-learning classification algorithms are compared. The linear support vector machine, distance-based classification, and k-nearest-neighbour algorithm were the classifiers used. The performance variations of the system under various illumination and focusing conditions are also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images. A micro fabricated cell sorter chip is then designed for the purpose of cell sorting using the above mentioned algorithm. A review of existing cytometry techniques is conducted to justify the choices of detection and flow control technologies. Then the chip structure is designed. Experiments are conducted with different channel dimensions and chip layouts to optimize the fabrication process and sample focusing performances, a sorting simulation is conducted using fluorescent beads to optimize the detection system parameters and verify the sorting accuracy. A cell counting experiment is also performed, the system was able to detect and classify cells with very high accuracy, with a throughput of 1.5 cells per second. Due to equipment and time limitation, cell sorting was not verified. This thesis project shows the goal of implementing mLSI at Waterloo Microfluidic Laboratory was successfully achieved, and imaging detection and mLSI can be used to produce a cell sorter capable of detecting and classifying yeast cells in different cell cycle phases. Recommendations are made at the end for improvements in the mLSI system, and the application of the cell sorter in detecting protein factors in budding yeast cells

Particles Separation in Microfluidic Devices, Volume II

Microfluidic platforms are increasingly being used for separating a wide variety of particles based on their physical and chemical properties. In the past two decades, many practical applications have been found in chemical and biological sciences, including single cell analysis, clinical diagnostics, regenerative medicine, nanomaterials synthesis, environmental monitoring, etc. In this Special Issue, we invited contributions to report state-of-the-art developments in the fields of micro- and nanofluidic separation, fractionation, sorting, and purification of all classes of particles, including, but not limited to, active devices using electric, magnetic, optical, and acoustic forces; passive devices using geometries and hydrodynamic effects at the micro/nanoscale; confined and open platforms; label-based and label-free technology; and separation of bioparticles (including blood cells), circulating tumor cells, live/dead cells, exosomes, DNA, and non-bioparticles, including polymeric or inorganic micro- and nanoparticles, droplets, bubbles, etc. Practical devices that demonstrate capabilities to solve real-world problems were of particular interest

Modeling of the MEMS Reactive Ion Etching Process Using Neural Networks

Abstract Reactive ion etch (RIE) is commonly used in microelectromechanical systems (MEMS) fabrication as plasma etching method, where ions react with wafer surface substrate in plasma environment. Due to the importance of RIE in the MEMS field, two prediction models are established to predict the wafer status in reactive ion etching process: back-propagation neural network (BPNN) and principle component analysis BPNN (PCABPNN). These models have the potential to reduce the overall cost of ownership of MEMS equipment by increasing the wafer yield, and not depend upon monitoring wafers or expensive metrology rather it will enable inexpensive real-time wafer-to-wafer control applications in RIE. The artificial neural net (ANN) is trained with historical available input-output process data. Once trained, the ANN forecasts the process output rapidly if given the input values