Search query: semiconductor machine learning

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abstract: A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer Aided Design (TCAD) augmented ML for semiconductor structural variation identification and inverse design, without using domain expertise. TCAD augmented ML utilizes TCAD simulations to generate sufficient data for ML model development when experimental data are inadequate. The ML model can then be used to identify semiconductor structural variation for given experimental electrical measurements. In this study, the variation of layer thicknesses in the p i n diode is used as a demonstration. An ML model is developed to predict the diode layer thicknesses based on a given Current Voltage (IV) curve. Although the variations of interest can be incorporated easily in TCAD simulations to generate ML training data, the TCAD augmented ML model generally is overfitted and cannot predict the variations in experiment well due to hidden variables which also alters the IV curves. We show that by using an autoencoder, this problem can be solved. To verify the effectiveness, another set of TCAD simulation data is generated with hidden variables (dopant concentration variation) to emulate experimental data. Testing on the second set of data shows that the proposed model can avoid overfitting and has up to 15 times improvement in accuracy in thickness prediction. Moreover, this model is used successfully to perform inverse design and can capture an underlying physics that cannot be described by a simple physical parameter.
author_names: Kashyap Mehta

Sophia Susan Raju

Ming Xiao

Boyan Wang

Yuhao Zhang

Hiu Yung Wong
title: Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design
venue: IEEE Access
year: 2020

abstract: Abstract This present work utilizes the distortion due to the incorporation of dopants into crystal lattice structure of zinc oxide (ZnO) semiconductor to model the associated energy band gap. The proposed model hybridizes machine learning support vector regression model (MLSVRM) and gravitational search algorithm (GSA) that is based on Newtonian mechanical principle of motion. The performance of the developed hybrid gravitational search (GS) based MLSVRM is compared with stepwise regression (ST) based model and the existing ordinary support vector regression computational intelligence (SVRCI) model in the literature. The developed hybrid GS MLSVRM performs better than ST based model and the existing SVRCI model in the literature by 83.74% and 42.59% respectively using mean absolute percentage deviation (MAPD) as a performance measuring parameter. The developed hybrid GS MLSVRM further shows superior performance over the two compared models using other performance measuring parameters such as root mean square error (RMSE) mean absolute error (MAE) and correlation coefficient (CC) The superior performance of the developed hybrid model can be attributed to the power of hybridization and intrinsic ability to approximate non linear relationship between lattice distortion and energy band gap of the doped semiconductor. The precision of the developed hybrid model would ultimately hasten ZnO band gap characterization without experimental stress.
author_names: Sami M Ibn Shamsah

Taoreed Olakunle Owolabi
title: Newtonian mechanics based hybrid machine learning method of characterizing energy band gap of doped zno semiconductor
year: 2020

abstract: Advanced data analysis tools and techniques are important for semiconductor companies to gain competitive advantage. In particular, yield prediction tools, which fully utilize production data, help to improve operational efficiency and reduce production costs. This paper introduces a novel and scalable framework for semiconductor manufacturing Final Test (FT) yield prediction leveraging machine learning techniques. This framework is able to predict FT yield at wafer fabrication stage, so that FT low yield problems can be caught at an earlier production stage compared to past studies. Our work presents a robust solution to automatically handle both numerical and categorical production related data without prior knowledge of the low yield root cause. Gaussian Mixture Models, One Hot Encoder and Label Encoder techniques are adopted for data pre processing. To improve model performance for both binary and multi class classification, model selection and model ensemble using the F1 macro method is demonstrated. The framework has been applied to three mass production products with different wafer technologies and manufacturing flows. All of them achieved high F1 macro test score indicative of the robustness of our framework.
author_names: Dan Jiang

Weihua Lin

Nagarajan Raghavan
title: A Novel Framework for Semiconductor Manufacturing Final Test Yield Classification Using Machine Learning Techniques
venue: IEEE Access
year: 2020

abstract: In this article, the authors attempt to describe the core quality inspection during semiconductor manufacturing in terms of production efficiency and yield. Special focus is therefore given to photolithography, which is the most critical step for the fabrication of wafer patterns in front end processes. Further, machine learning approaches are demonstrated and their applicability in semiconductor manufacturing industry is discussed. Also, a technical concept regarding virtual metrology for advanced process control in semiconductor production is introduced as a potential utilization case. Finally, current status and future trends in technology as well as application are summarized based on authors' perspective in the concluding section.
author_names: Lei Wang
title: Application of Machine Learning for Process Control in Semiconductor Manufacturing
year: 2020

abstract: Maintenance in manufacturing has been developed and researched in the last few decades at a very rapid rate. It's a major step in process control to build a decision tool that detects defects in equipment or processes as quickly as possible to maintain high process efficiencies. However, the high complexity of machines, and the increase in data available in almost all areas, makes research on improving the accuracy of fault detection via data mining more and more challenging issue in this field. In our paper we present a new predictive model of semiconductor failures, based on machine learning approach, for predictive maintenance in industry 4.0. The framework of our model includes: Dataset and data acquisition, data preprocessing in three phases (over sampling, data cleaning, and attribute reduction with principal component analysis (PCA) technique and CfsSubsetEval technique) data modeling, evaluation model and implementation model. We used SECOM dataset to develop four different models based on four algorithms (Naive Bayesian, C4.5 Decision tree, Multilayer perceptron (MLP) Support vector machine) according to the five metrics (True Positive rate, False Positive rate, Precision, F Mesure and Accuracy) We implemented our new predictive model with 91, 95% of accuracy, as a new efficient predictive model of semiconductor failures. Keywords Machine learning; semiconductor; predictive maintenance; industry 4.0
author_names: Yousef El Mourabit

Youssef El

Hicham Zougagh

Younes Wadiai
title: Predictive System of Semiconductor Failures based on Machine Learning Approach
year: 2020

abstract: While spin qubits based on gate defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine tuning of quantum dots, and demonstrate its performance on a semiconductor singlet triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab time.
author_names: Julian D Teske

Simon Sebastian Humpohl

Ren'e Otten

Patrick Bethke

Pascal Cerfontaine

Jonas Dedden

Arne Ludwig

Andreas Dirk Wieck

Hendrik Bluhm
title: A machine learning approach for automated fine tuning of semiconductor spin qubits
venue: Applied Physics Letters
year: 2019

abstract: In this paper, we show the possibility of using Technology Computer Aided Design (TCAD) to assist machine learning for semiconductor device failure trouble shooting and device reverse engineering. When TCAD simulation models and parameters are properly chosen and calibrated, large number of devices with random defects and structural characteristics can be generated and simulated. The results can then be used to train machine learning algorithms to predict the defect and structural characteristics of a device with given electrical characteristics (such as IV's and CV's) 1D PIN diode with various layer thicknesses and doping concentrations are used in this study. It is showed that with less than 2000 training samples, by using simple linear regression, one can achieve good prediction of layer thickness and doping of a given IV curve.
author_names: Y S Bankapalli

Hiu Yung Wong
title: TCAD Augmented Machine Learning for Semiconductor Device Failure Troubleshooting and Reverse Engineering
venue: 2019 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
year: 2019

abstract: Semiconductor cluster equipment adds an integral component to the modern semiconductor manufacturing process. These complex tools provide a flexible deployment option to group multiple processing steps into a single piece of equipment, allowing for more efficient processing. They also contribute to a reduction in the number of times a wafer must go through the atmospheric vacuum atmospheric cycle. Such highly automated tools present a complex scheduling challenge where process specific requirements are balanced against a need to achieve maximum wafer throughput in a fault tolerant manner. Software engineers typically build schedulers using a set of manually configured heuristics but this can be a labor intensive process where small changes to the cluster configuration or process requirements can require large changes to the scheduler. Our motivation for this work was to investigate whether a machine learning approach to complex cluster scheduling could be developed more efficiently and at a lower cost than existing methods.
author_names: Doug Suerich

Terry Young
title: Machine Learning for Optimized Scheduling in Complex Semiconductor Equipment
venue: 2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2019

abstract: Abstract The progress of digitalization enables new potentials to supply chain management by available data as well as by analysis methods like machine learning. This paper focuses on the master production planning matching demand and supply for a midterm time horizon, in a volatile, diverse and capacity constrained environment. Therefore, a framework for measuring instability is outlined, a machine learning approach to predict instability is developed and applied using the CRISP DM methodology on real data of a semiconductor manufacturer. The evaluation and results foster the concept and the field of application, but request the next step of prescriptive instability minimization.
author_names: Tim Lauer

Sarah Legner

Michael Henke
title: Application of machine learning on plan instability in master production planning of a semiconductor supply chain
year: 2019

abstract: In this work, we present a unique approach of combining TCAD modelling and machine learning to detect the defect locations of a bridging defect in a single fin FinFET. The prediction of the defect location is guided by the predictive model consisting of Random Forest algorithm which is trained with the measureable electrical attributes from the I V. High accuracy in predicting the defect location is achieved by the proposed scheme which can further enhance the FA success rate, expediting the cycle of design to product.
author_names: C W Teo

Kain Lu Low

Vinod Narang

Aaron Voon-Yew Thean
title: TCAD Enabled Machine Learning Defect Prediction to Accelerate Advanced Semiconductor Device Failure Analysis
venue: 2019 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
year: 2019