Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems

Park, Sung Joo; Bae, Bumhee; Kim, Joungho; Swaminathan, Madhavan

doi:10.1109/tvlsi.2017.2656843

Cited by 65 publications

(21 citation statements)

References 11 publications

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“…The observation samples were selected by the built-in acquisition function during the fitting process. The most commonly used acquisition functions are expected improvement (EI), probability of improvement (PI), and upper/lower confidence bound (UCB/LCB) [ 24 ]. This function randomly selected thickness value, following a defined exploration-exploitation ratio (ER), within the thickness range and predicted the corresponding HUI values following the GP rule.…”

Section: Methodsmentioning

confidence: 99%

“…Microwaveable food geometry design can be considered as a parametric optimization problem, where the objective is to properly design the food parameters, such as shape and size, for an improved heating performance. In general parametric optimization, machine-learning has shown its capability and advantages as an efficient method to ‘self-train’ and ‘speculate’ based on limited (training) data [ 23 , 24 , 25 ]. Machine-learning models can be developed to reveal the relationship between data inputs and outputs of a system [ 26 ] and identify the proper inputs that could generate the desired outputs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The integrated optimization approach that combines mechanistic-modeling and machine-learning has been applied in various fields for parameter optimization, such as electronic system design [ 24 ], additive manufacture [ 28 ], solid mechanics [ 29 ], fluid flow [ 30 ], precision medicine [ 31 ], and biomedical engineering [ 32 ]. For example, by coupling machine-learning with electric-thermal simulation, the optimal conditions (e.g., airflow, material, and geometry) for 3-D integrated circuits and systems were explored by Park et al [ 24 ]. In that study, a Bayesian optimization-based machine-learning approach was successfully developed to optimize the operation parameters and system designs, including airflow velocity, thermal conductivity of the thermal interface material, thermal conductivity of under-fill material, thermal conductivity of the printed circuit board, and thickness of thermal interface material, to minimize the maximum temperature and temperature gradient.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Yang

Wang

Chen

2021

Foods

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Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Yang

Wang

Chen

2021

Foods

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“…M ACHINE learning (ML) techniques have found applications in many research areas including engineering and biomedical applications [1]- [5]. Recently, several studies have also employed ML methods for signal propagation prediction and antenna design applications.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Approach for Diffraction Loss Variation Prediction by the Human Body

Khalily

Brown

Tafazolli

2019

Antennas Wirel. Propag. Lett.

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This paper presents a machine learning (ML) based model to predict the diffraction loss around the human body. Practically, it is not reasonable to measure the diffraction loss changes for all possible body rotation angles, builds and line of sight (LoS) elevation angles. A diffraction loss variation prediction model based on a non-parametric learning technique called Gaussian process (GP) is introduced. Analysed results state that 86% correlation and normalised mean square error (NMSE) of 0.3 on the test data is achieved using only 40% of measured data. This allows a 60% reduction in required measurements in order to achieve a well-fitted ML loss prediction model. It also confirms the model generalizability for non-measured rotation angles.Index Terms-Diffraction loss, guassian process (GP), machine learning (ML), network planning tool.

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Rapid heat source layout optimization in three‐dimensional integrated circuits using artificial neural network reduced‐order model in combination with Bayesian optimization

Zhang,

Song,

Rao

et al. 2024

Heat Trans

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In this study, an efficient optimization framework was developed to determine the parameters of through‐silicon vias and the layout of heat sources in three‐dimensional integrated circuits (3D ICs), employing an artificial neural network (ANN) reduced‐order model in conjunction with a Bayesian optimization (BO) algorithm. The proposed method effectively predicts the temperature distribution in 3D ICs and refines their thermal parameters, offering solutions to thermal management challenges. Latin hypercube sampling was utilized for data sampling, enhancing the previously established rapid thermal analysis method through parameterization of heat source locations. The temperature distribution data for varying hotspot locations in 3D ICs were fitted using an appropriately defined objective function, leading to the development of a reduced‐order ANN model that accelerates temperature prediction. The computational results demonstrate that the neural network model exhibits a deviation in predicted values of less than 2%, and the coefficient of determination R2 approximately 0.93, underscoring high predictive accuracy. Additionally, the optimization outcomes and the efficiency of the selected BO algorithm were thoroughly evaluated. Notably, the BO algorithm achieved the global optimum in just 4.07 s across 250 iterations, demonstrating an effective power distribution strategy for the 3D ICs model.

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Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems

Cited by 65 publications

References 11 publications

An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Machine-Learning-Based Approach for Diffraction Loss Variation Prediction by the Human Body

Rapid heat source layout optimization in three‐dimensional integrated circuits using artificial neural network reduced‐order model in combination with Bayesian optimization

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