Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer

Yella, Jaswanth; Zhang, Chao; Петров, Сергей Александрович; Huang, Yu; Qian, Xiaoye; Minai, Ali A.; Bom, Sthitie

doi:10.1109/bigdata52589.2021.9671991

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Soft Sensing Transformer (SST) [20] demonstrates the similarities between sensor readings and text data, and applies Transformer encoder [3] into this task. ConFormer [21] integrates the structures of both CNN and Transformer. Soft Sensing Model Visualization [22] put more focus on model interpretation.…”

Section: Related Workmentioning

confidence: 99%

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

Fan¹,

Huang²,

Fei³

et al. 2023

Preprint

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The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many semiconductor manufacturing equipments are equipped with sensors to facilitate real-time monitoring of the production process. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the task of soft sensing regression, which uses sensor data to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed an LSTM-based regressor and designed two loss functions for model training. Although engineers may look at our prediction errors in a subjective manner, a new piece-wise evaluation metric was proposed for assessing model accuracy in a mathematical way. The experimental results demonstrated that the proposed model can achieve accurate and early prediction of various types of inspections in complicated manufacturing processes.

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Section: Related Workmentioning

confidence: 99%

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

Fan¹,

Huang²,

Fei³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, features are chosen [16] and methodslikePCA (principal component analysis) [17] or LDA (linear discriminant analysis) [18] are typically employed to reduce dimensionality of these features. In addition, [19], for example, used data entropy to preprocess raw time series data.RPCA (Recursive PCA) [20], DPCA (dynamic PCA) [21] and KPCA (kernel PCA) [22] are used to monitor a variety of industrial processes, including adaptive, dynamic, and nonlinear processes [24]. Two RPCA algorithms were published in [25] to adapt for regular process changes in semiconductor production operations.…”

Section: Related Workmentioning

confidence: 99%

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Wongchai¹,

Abourehab

Ahmed

et al. 2023

Int J Adv Manuf Technol

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In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. Once soft sensors are designed, a mechanism to maintain or update these models is highly desirable in industry. This paper proposed novel technique in monitoring and control optimization of soft sensors in automation industry for fault detection. Here fault detection has been carried out using Probabilistic Multi-Layer Fourier Transform Perceptron (PMLFTP). The input collected plant data has been pre-processed for removal of samples containing null values.Then fault detection and diagnosis process have been carried out based on probability of the data with Fourier transform-based detection and multi-layer perceptronbased diagnosis of the fault in the manufacturing process. Then controlling of data in soft sensors has been optimized using auto-regression based ant colony optimization (AR_ACO) which has effect in increasing the production of industry automatically. The experimental results have been carried out in terms of computational rate of 40%, QoS of 78%, RMSE of 45%, fault detection rate of 90%, control optimization of 93% has been obtained for various historical data-based evaluations.

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“…One such model, Soft-sensing Transformer (SST) [21], utilizes a Transformer encoder [3] to demonstrate the similarities between sensor readings and text data. Another model, ConFormer [22], leverages multi-head convolution modules to achieve fast and lightweight operations while still being able to learn robust representations through multi-head design, similar to transformers. Soft-sensing Model Visualization [23] fine-tunes the model by adjusting the weights of input features based on misclassified examples.…”

Section: Related Workmentioning

confidence: 99%

Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting

Fan,

Huang,

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many types of semiconductor manufacturing equipments have been equipped with sensors to facilitate real-time monitoring of the production processes. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the soft-sensing regression problem in metrology systems, which uses sensor data collected during wafer processing steps to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed a regressor based on Long Short-term Memory network and devised two distinct loss functions for the purpose of the training model. Although the assessment of our prediction errors by engineers is subjective, a novel piece-wise evaluation metric was introduced to evaluate model accuracy in a mathematical way. Our experimental results showcased that the proposed model is capable of achieving both accurate and early prediction across various types of inspections in complicated manufacturing processes.

show abstract

Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer

Cited by 9 publications

References 20 publications

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting

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