scholarly journals Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3005
Author(s):  
Jiwon Kwon ◽  
Sangwon Ryu ◽  
Jihoon Park ◽  
Haneul Lee ◽  
Yunchang Jang ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Etch Depth ◽  
Statistical Regression ◽  
Process Data ◽  
Optical Emission Spectrum ◽  
Area Model ◽  
Virtual Metrology ◽  
Profile Control ◽  
Etch Profile ◽  
Rectangular Model

In the semiconductor etch process, as the critical dimension (CD) decreases and the difficulty of the process control increases, in-situ and real-time etch profile monitoring becomes important. It leads to the development of virtual metrology (VM) technology, one of the measurement and inspection (MI) technology that predicts the etch profile during the process. Recently, VM to predict the etch depth using plasma information (PI) variables and the etch process data based on the statistical regression method had been developed and demonstrated high performance. In this study, VM using PI variables, named PI-VM, was extended to monitor the etch profile and investigated the role of PI variables and features of PI-VM. PI variables are obtained through analysis on optical emission spectrum data. The features in PI-VM are investigated in terms of plasma physics and etch kinetics. The PI-VM is developed to monitor the etch depth, bowing CD, etch depth times bowing CD (rectangular model), and etch area model (non-rectangular model). PI-VM for etch depth and bowing CD showed high prediction accuracy of R-square value (R2) 0.8 or higher. The rectangular and non-rectangular etch area model PI-VM showed prediction accuracy R2 of 0.78 and 0.49, respectively. The first trial of virtual metrology to monitor the etch profile will contribute to the development of the etch profile control technology.

scholarly journals Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Muddu Madakyaru ◽  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Feature Extraction ◽  
Latent Variable ◽  
Prediction Accuracy ◽  
Distillation Column ◽  
Selection Criterion ◽  
Process Data ◽  
Distillation Columns ◽  
Latent Variable Regression ◽  
Multiscale Filtering ◽  
Modeling Algorithm

Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.

scholarly journals Multiscale Latent Variable Regression

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Latent Variable ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Multiple Scales ◽  
Principal Component Regression ◽  
Principal Component ◽  
Measurement Noise ◽  
Process Data ◽  
Latent Variable Regression ◽  
Modeling Algorithm

Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regression models at multiple scales using the scaled signal approximations of the data and then using cross-validation, and select among all MSLVR models the model which best describes the process. The main advantage of the MSLVR modeling algorithm is that it inherently accounts for the presence of measurement noise in the data by the application of the low-pass filters used in multiscale decomposition, which in turn improves the model robustness to measurement noise and enhances its prediction accuracy. The advantages of the developed MSLVR modeling algorithm are demonstrated using a simulated inferential model which predicts the distillate composition from measurements of some of the trays' temperatures.

scholarly journals Etch profile control of high-aspect ratio deep submicrometer α-Si gate etch

2001 ◽  
Vol 14 (3) ◽  
pp. 242-254 ◽  
Author(s):  
Hyun-Mog Park ◽  
D.S. Grimard ◽  
J.W. Grizzle ◽  
F.L. Terry
Keyword(s):  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Profile Control ◽  
Deep Submicrometer ◽  
Etch Profile

Etch Profile Control of W/TiN/HfSiON and W/TaSiN/HfSiON Full-Metal Gates

2009 ◽  
Vol 48 (11) ◽  
pp. 116513 ◽  
Author(s):  
Tetsuo Ono ◽  
Takashi Aoyama ◽  
Yasuo Nara
Keyword(s):  
Metal Gates ◽  
Profile Control ◽  
Etch Profile

Virtual metrology for prediction of etch depth in a trench etch process

2013 ◽  
Author(s):  
Georg Roeder ◽  
Martin Schellenberger ◽  
Lothar Pfitzner ◽  
Sirko Winzer ◽  
Stefan Jank
Keyword(s):  
Etch Depth ◽  
Virtual Metrology

scholarly journals Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

2021 ◽  
Vol 11 (4) ◽  
pp. 1549
Author(s):  
Han-Jui Chang ◽  
Guang-Yi Zhang ◽  
Zhi-Ming Su ◽  
Zhong-Fa Mao
Keyword(s):  
Performance Evaluation ◽  
Process Parameters ◽  
Prediction Method ◽  
Cutting Conditions ◽  
Polymer Materials ◽  
Process Data ◽  
Crystalline Polymers ◽  
Combined Application ◽  
Virtual Metrology ◽  
Process Prediction

One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.

scholarly journals A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yao Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Production Line ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Fault Prediction ◽  
Industrial Processes ◽  
Series Data ◽  
Process Data

Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However, in the face of complex industrial processes, this problem will become very difficult depending on traditional methods. In this paper, we propose a new approach based on a deep learning (DL) algorithm to solve the problem. First, we regard these process data as a spatial sequence according to the production process, which is different from traditional time series data. Second, we improve the long short-term memory (LSTM) neural network in an encoder-decoder model to adapt to the branch structure, corresponding to the spatial sequence. Meanwhile, an attention mechanism (AM) algorithm is used in fault detection and cause identification. Third, instead of traditional biclassification, the output is defined as a sequence of fault types. The approach proposed in this article has two advantages. On the one hand, treating data as a spatial sequence rather than a time sequence can overcome multidimensional problems and improve prediction accuracy. On the other hand, in the trained neural network, the weight vectors generated by the AM algorithm can represent the correlation between faults and the input data. This correlation can help engineers identify the cause of faults. The proposed approach is compared with some well-developed fault diagnosing methods in the Tennessee Eastman process. Experimental results show that the approach has higher prediction accuracy, and the weight vector can accurately label the factors that cause faults.

Sign in / Sign up

Export Citation Format

Share Document