Query: machine learning semiconductor optimization

Candidate papers:

abstract: With the globalization of the semiconductor manufacturing process, electronic devices are powerless against malicious modification of hardware in the supply chain. The ever increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods. Ring oscillator network (RON) is used to detect the Trojan by capturing the difference in power consumption; the power consumption of a Trojan free circuit is different from the Trojan inserted circuit. However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy. In this paper, we quantitatively compare four supervised machine learning algorithms and classifier optimization strategies for maximizing accuracy and minimizing the false positive rate (FPR) These supervised learning techniques show an improved false positive rate compared to principal component analysis (PCA) and convex hull classification by nearly 40% while maintaining 90% binary classification accuracy.
author_names: Kyle Worley

Md Tauhidur Rahman
title: Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network
venue: 2019 SoutheastCon
year: 2019

abstract: Abstract Atomic layer deposition (ALD) is a widely utilized deposition technology in the semiconductor industry due to its superior ability to generate highly conformal films and to deposit materials into high aspect ratio geometric structures. However, ALD experiments remain expensive and time consuming, and the existing first principles based models have not yet been able to provide solutions to key process outputs that are computationally efficient, which is necessary for on line optimization and real time control. In this work, a multiscale data driven model is proposed and developed to capture the macroscopic process domain dynamics with a linear parameter varying model, and to characterize the microscopic domain film growth dynamics with a feed forward artificial neural network (ANN) model. The multiscale data driven model predicts the transient deposition rate from the following four key process operating parameters that can be manipulated, measured or estimated by process engineers: precursor feed flow rate, operating pressure, surface heating, and transient film coverage. Our results demonstrate that the multiscale data driven model can efficiently characterize the transient input output relationship for the SiO2 thermal ALD process using bis(tertiary butylamino)silane (BTBAS) as the Si precursor. The multiscale data driven model successfully reduces the computational time from 0.6 to 1.2 h for each time step, which is required for the first principles based multiscale computational fluid dynamics (CFD) model, to less than 0.1 s, making its real time usage feasible. The developed data driven modeling methodology can be further generalized and used for other thermal ALD or similar deposition systems, which will greatly enhance the feasibility of industrial manufacturing processes.
author_names: Yangyao Ding

Yichi Zhang

Yi Ming Ren

Gerassimos Orkoulas

Panagiotis D Christofides
title: Machine learning based modeling and operation for ALD of SiO2 thin films using data from a multiscale CFD simulation
venue: Chemical Engineering Research and Design
year: 2019

abstract: In this paper, we present a novel interpretable machine learning technique that uses unique physical insights about noisy optical images and a few training samples to classify nanoscale defects in noisy optical images of a semiconductor wafer. Using this technique, we not only detected both parallel bridge defects and previously undetectable perpendicular bridge defects in a 9 nm node wafer using visible light microscopy [Proc. SPIE 9424, 942416 (2015) but we also accurately classified their shapes and estimated their sizes. Detection and classification of nanoscale defects in optical images is a challenging task. The quality of images is affected by diffraction and noise. Machine learning techniques can reduce noise and recognize patterns using a large training set. However, for detecting a rare "killer" defect, acquisition of a sufficient training set of high quality experimental images can be prohibitively expensive. In addition, there are technical challenges involved in using electromagnetic simulations and optimization of the machine learning algorithm. This paper proposes solutions to address each of the aforementioned challenges. (c) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
title: Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm
year: 2019

abstract: A Boltzmann machine whose effective "temperature" can be dynamically "cooled" provides a stochastic neural network realization of simulated annealing, which is an important metaheuristic for solving combinatorial or global optimization problems with broad applications in machine intelligence and operations research. However, the hardware realization of the Boltzmann stochastic element with "cooling" capability has never been achieved within an individual semiconductor device. Here we demonstrate a new memristive device concept based on two dimensional material heterostructures that enables this critical stochastic element in a Boltzmann machine. The dynamic cooling effect in simulated annealing can be emulated in this multi terminal memristive device through electrostatic bias with sigmoidal thresholding distributions. We also show that a machine learning based method is efficient for device circuit co design of the Boltzmann machine based on the stochastic memristor devices in simulated annealing. The experimental demonstrations of the tunable stochastic memristors combined with the machine learning based device circuit co optimization approach for stochastic memristor based neural network circuits chart a pathway for the efficient hardware realization of stochastic neural networks with applications in a broad range of electronics and computing disciplines.
author_names: Tong Wu

Huan Zhao

Fanxin Liu

Jing Guo

Han Wang
title: Tunable Stochastic Memristive Device for Simulated Annealing in Boltzmann Machine Designed by a Machine Learning Method
year: 2019

abstract: Due to the increasing complexity of semiconductor industry, the traditional rule based technology has faced its limits in solving Electronic Design Automation (EDA) problems, which have high dimensionality, discontinuous, and non linearities. Instead, machine learning (ML) has been in rapidly growing demand, and has been gradually involved in EDA applications [1] This talk shares several industrial applications of ML, including commercial EDA tools, and the great opportunity to expand the use of ML solution for design flow optimization as well.
author_names: Laura Wang

Matt Luo
title: Machine Learning Applications and Opportunities in IC Design Flow
venue: 2019 International Symposium on VLSI Design, Automation and Test (VLSI DAT)
year: 2019

abstract: IC Design has been an industry which provides flexible application specific integrated circuit (ASIC) services enabling semiconductor manufacturing companies for flexible decision. Although the industry influences semiconductor supply chain significantly, capacity portfolio and planning issues of IC design industry is seldom mentioned in the past studies. For IC design service industry, the main productivity denotes to IC design which is influenced by the performance of project management from workforce allocation. The purpose of this study is to develop an intelligent agent to predict the workforce required for each wafer production service project, and thus based on the prediction, the intelligent agent is able to provide an IC design service company with workforce allocation strategy. Featuring learning algorithms and analyzing from the existing data, the study trains a XG Boosting model combining Genetic Algorithm based parameter optimization mechanism. The proposed intelligent agent contributes to Total Resource Management (TRM) to enhance productivity, reduce costs and intelligence management.
author_names: Chieh Hsu

Andy Y F Kuo

Ju-Chien Chien

Wenhan Fu

Kang-Ting Ma

Chen-Fu Chien
title: A Machine Learning based Intelligent Agent for Human Resource Planning in IC Design Service Industry
year: 2019

abstract: Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross scale heuristics developed by experimentalists is thus of great importance. Materials and methods To implement the machine learning based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. Results and discussion From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is 96.5% when using either current based or charge sensor based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current and charge sensor based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite a Python based mini software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.
author_names: Justyna P Zwolak

Sandesh S Kalantre

Xingyao Wu

Stephen Ragole

Jacob M Taylor
title: QFlow lite dataset: A machine learning approach to the charge states in quantum dot experiments
venue: PloS one
year: 2018

abstract: As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC and assist feature (AF) optimization methods for each subsequent process technology. Several publications and industry presentations have discussed the use of neural networks or other machine learning techniques to provide improvements in efficiency for OPC main feature optimization or AF placement. In this paper, we present results of a method for using machine learning to predict OPC mask segment displacements. We will review several detailed examples showing the accuracy and overall OPC TAT benefits of our method for advanced node manufacturing test cases. We will also discuss the experiments testing the amount and diversity of training data required to achieve true production level OPC stability.
author_names: Hesham M Abdelghany

Kevin Hooker

Marco Guajardo

Chia-Chun Lu
title: Implementing Machine Learning OPC on product layouts
year: 2020

abstract: Maximizing yield in a modern semiconductor fab requires proper optimization of the design (layout) process technology, and fab process tool recipes. For the past decade the prevalence of systematic defects tied to design or design process interactions have predominated over random defect sources. Previously Resolution Enhancement Technology (RET) Design For Manufacturability (DFM) and Design Technology Co optimization (DTCO) techniques were the successful response to eliminating systematic yield limiting patterns. Machine learning, with its ability to find trends and make predictions based on large volumes of data, provides a unique path towards further reduction in systematic defect levels. This talk will present methods based on the use of design and process info with machine learning and computational lithography methods to identify and eliminate yield limiting patterns in the design, improve the accuracy of mask generation with etch and resist modeling and OPC, and improve the productivity and accuracy of fab defect detection and diagnostics. This paper will present methods to improve EPE control and reduce systematic hotspots through both supervised and unsupervised machine learning. Specifically we will focus on 3 areas: identifying and yield limiting patterns in the design phase. improving the accuracy (EPE control) of mask generation with machine learning assisted etch and resist modeling and OPC. improving the productivity and accuracy of fab defect detection and diagnostics with machine learning.
author_names: Yuansheng Ma

Le Van Hong

James C Word

Fan Jiang

Vlad Liubich

Liang Cao

Srividya Jayaram

Doohwan Kwak

Youngchang Kim

Germain Fenger

Ananthan Raghunathan

Joerg Mellmann
title: Reduction of systematic defects with machine learning from design to fab
venue: Advanced Lithography
year: 2020

abstract: As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC and assist feature (AF) optimization methods for each subsequent process technology. To meet manufacturing yield requirements, significant wafer retargeting from the original design target is often performed before OPC to account for both lithographic limitations and etch effects. As retargeting becomes more complex and important, rule table based approaches become ineffective. Alternatively, modelbased optimization approaches using advanced solvers, e.g. inverse lithography technology (ILT) have demonstrated process window improvement over rule based approaches. However, model based target optimization is computationally expensive which typically limits its use to smaller areas like hotspot repairs. In this paper, we present results of a method that uses machine learning (ML) to predict optimal retargeting for line space layers. In this method, we run ILT co optimization of the wafer target and process window to generate the training data used to train a machine learning model to predict the optimum wafer target. We explore methods to avoid ML model overfitting and show the ML infrastructure used to integrate ML solution into a manufacturable OPC flow. Both lithographic quality and runtime performance are evaluated for an ML enabled retargeting flow, an ILT flow and a simple rule table flow at advanced node test cases.
author_names: Kevin Hooker

Marco Guajardo

Nai-Chia Cheng

Guangming Xiao
title: Implementing Machine Learning for OPC retargeting
year: 2020