Search query: machine learning semiconductor thermal

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abstract: Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved) The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies. To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate defined quantum dot devices, showing about 180 times faster than a pure random search.
author_names: H Moon

Dominic T Lennon

J Kirkpatrick

N M van Esbroeck

Leon C Camenzind

Liuqi Yu

Florian Vigneau

Dominik M Zumbuhl

G Andrew D Briggs

Michael A Osborne

D Sejdinovic

E A Laird

Natalia Ares
title: Machine learning enables completely automatic tuning of a quantum device faster than human experts
venue: Nature Communications
year: 2020

abstract: Power chips such as Metal Oxide Field Effect Transistors (MOSFETs) are widely used and can be found in many electronics and electrical products. The ability to predict the degradation of such power electronic devices can minimise the risk of their failure during operation and support maintenance planning operations. In this study, a data driven prognostics approach using system identification and machine learning modelling technique is developed and used to predict the time to failure of MOSFET TO 220 packages associated with delamination failure mode of the die attachment. Run to failure data under thermal overstress loading conditions for power chip devices, available from the NASA Prognostics Centre data repository, is used to develop a data driven prognostic model that can be used to predict the time to failure (TtF) of power MOSFETs under accelerated test loads. An increment in ON state resistance of the MOSFET is used as precursor for device failure through die attach degradation. Results from this research show that when monitored data from a damage indicator for a particular failure mode of an electronic package changes dynamically, data driven modelling using engineering control techniques such as State Space representation is capable of producing reliable, multi step ahead predictions for the time to failure of the device.
author_names: Mominul Ahsan

Stoyan Stoyanov

Chris Bailey
title: Data Driven Prognostics for Failure of Power Semiconductor Packages
venue: 2018 41st International Spring Seminar on Electronics Technology (ISSE)
year: 2018

abstract: Abstract Plasma enhanced atomic layer deposition (PEALD) is one of the most widely adopted deposition methods used in the semiconductor industry. It is chosen largely due to its superior ability to produce ultra thin high k dielectric films, which are needed for the further miniaturization of microelectronic devices with the pace of Moore's Law. In contrast to the traditional thermal atomic layer deposition (ALD) method, PEALD allows for high deposition growth per cycle (GPC) under low operating temperature with the help of high energy plasma species. Despite the experimental effort in finding new precursors and plasmas, the detailed surface structures and reaction mechanisms in various PEALD processes remain hard to understand because of the limitation of current in situ monitoring techniques and the deficiency of the first principles based analysis. Therefore, in this work, an accurate, yet efficient kinetic Monte Carlo (kMC) model and an associated machine learning (ML) analysis are proposed to capture the surface deposition mechanism and to propose optimal operating conditions of the HfO2 thin film PEALD using tetrakis dimethylamino Hafnium (TDMAHf) and oxygen plasma. Density Functional Theory (DFT) calculations are performed to obtain the key kinetic parameters and the structural details, subsequently employed in the kMC model. After the kMC model is validated by experimental data, a database is generated to explore a variety of precursor partial pressure and substrate temperature combinations using the kMC algorithm. A feed forward Bayesian regularized artificial neural network (BRANN) is then constructed to characterize the input output relationship and to investigate the optimal operating conditions.
author_names: Yangyao Ding

Yichi Zhang

Gerassimos Orkoulas

Panagiotis D Christofides
title: Microscopic modeling and optimal operation of plasma enhanced atomic layer deposition
year: 2020

abstract: As the micro electro mechanical machine (MEMS) industry is maturing, an increased array of product applications and devices are being introduced. As these devices are being developed new processes are required to control and attain the desired levels of polysilicon stress. In this work the relationship between polysilicon blanket residual stress and dopant concentration and anneal conditions are investigated. It was found that as the sheet resistance increased, the magnitude of the stress increased several orders of magnitude. Annealing the wafers for increased durations and multiple cycles lowered the level of stress observed while reducing the response to dopant concentration. The response was reduced by fifty percent for a time increase from 20 to 180 minutes. It is suggested that specific levels of stress are best attained with modifications to the dopant concentration for the required thermal cycles. The characterization performed allows for reduced learning cycles and cost in the development of new MEMS process flows to achieve first pass success for device specific requirements.
author_names: Jerzy Woloszyn
title: A method for controlling residual film stress in LPCVD polysilicon films for surface micromachined MEMS
venue: 2004 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (IEEE Cat. No.04CH37530)
year: 2004

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: Understanding the breakdown mechanisms of polymer based dielectrics is critical to achieving high density energy storage. Here a comprehensive phase field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer based dielectrics. High throughput simulations are performed for the P(VDF HFP) based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF HFP) based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.Polymer dielectrics are promising for high density energy storage but dielectric breakdown is poorly understood. Here, a phase field model is developed to investigate electric, thermal, and mechanical effects in the breakdown process for a range of polymer dielectrics, and analytical expression for breakdown strength is provided by machine learning.
author_names: Zhonghui Shen

Jian-Jun Wang

Jian-yong Jiang

Sharon X Huang

Yuanhua Lin

Ce-Wen Nan

Long-Qing Chen

Yang Shen
title: Phase field modeling and machine learning of electric thermal mechanical breakdown of polymer based dielectrics
venue: Nature Communications
year: 2019

abstract: Abstract This work introduces a new technique that provides real time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non invasive approach for automatic prediction of personal thermal comfort and mean time to warm discomfort using machine learning. The prediction framework described uses temperature information extracted from multiple local body parts to model an individual's thermal preference, with sensing measurements that capture local body part variance as well as differences between body parts. We compared the efficacy of using machine learning with classical measurements such as skin temperature along with our approach of using multi part measurements and derived data. An analysis of the performance of machine learning shows that our method improved the accuracy of personal thermal comfort prediction by an average of 60% and the accuracy of mean time to warm discomfort prediction by an average of 40% The proposed thermal models were tested on subjects' data extracted from an office setup with room temperature varying from low (21.11 degC) to high (27.78 degC) When all proposed features were used, personal thermal comfort was predicted with an accuracy higher than 80% and mean time to warm discomfort with more than 85% accuracy. Further analysis of the machine learning efficacy showed that temperature differences had the highest impact on performance of individual thermal preference prediction, while the proposed approach was found not sensitive to the actual machine learning algorithm.
author_names: Andrei Claudiu Cosma

Rahul Simha
title: Machine learning method for real time non invasive prediction of individual thermal preference in transient conditions
year: 2019

abstract: Abstract Thermal fluid processes are inherently multi physics and multi scale, involving mass momentum energy transport phenomena at multiple scales. Thermal fluid simulation (TFS) is based on solving conservative equations, for which except for "first principles" direct numerical simulation closure relations (CRs) are required to provide microscopic interactions or so called sub grid scale physics. In practice, TFS is realized through reduced order modeling, and its CRs as low fidelity models can be informed by observations and data from relevant and adequately evaluated experiments and high fidelity simulations. This paper is focused on data driven TFS models, specifically on their development using machine learning (ML) Five ML frameworks are introduced including physics separated ML (PSML or Type I ML) physics evaluated ML (PEML or Type II ML) physics integrated ML (PIML or Type III ML) physics recovered (PRML or Type IV ML) and physics discovered ML (PDML or Type V ML) The frameworks vary in their performance for different applications depending on the level of knowledge of governing physics, source, type, amount and quality of available data for training. Notably, outlined for the first time in this paper, Type III models present stringent requirements on modeling, substantial computing resources for training, and high potential in extracting value from "big data" in thermal fluid research. The current paper demonstrates and investigates ML frameworks in three examples. First, we utilize the heat diffusion equation with a nonlinear conductivity model formulated by convolutional neural networks (CNNs) and feedforward neural networks (FNNs) to illustrate the applications of Type I, Type II, Type III, and Type V ML. The results indicate a preference for Type II ML under deficient data support. Type III ML can effectively utilize field data, potentially generating more robust predictions than Type I and Type II ML. CNN based closures exhibit more predictability than FNN based closures, but CNN based closures require more training data to obtain accurate predictions. Second, we illustrate how to employ Type I ML and Type II ML frameworks for data driven turbulence modeling using reference works. Third, we demonstrate Type I ML by building a deep FNN based slip closure for two phase flow modeling. The results show that deep FNN based closures exhibit a bounded error in the prediction domain.
author_names: Chih-Wei Chang

Nam T Dinh
title: Classification of machine learning frameworks for data driven thermal fluid models
year: 2019

abstract: The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine learning assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18 0.41 W/mK, which are comparable to those of state of the art polymers in non composite thermo plastics.
author_names: Stephen Wu

Yukiko Kondo

Masa-aki Kakimoto

Bin Yang

Hironao Yamada

Isao Kuwajima

Guillaume Lambard

Kenta Hongo

Yibin Xu

Junichiro Shiomi

Christoph Schick

Junko Morikawa

Ryo Yoshida
title: Machine learning assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
venue: npj Computational Materials
year: 2019

abstract: Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high dimensional mathematical problem. Machine learning is a cost effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96% Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top 100 high ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra low thermal conductivity in our previous work. We believe that the predicted high ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning.
author_names: Yen-Ju Wu

Lei Fang

Yibin Xu
title: Predicting interfacial thermal resistance by machine learning
venue: npj Computational Materials
year: 2019