Duane Boning

Professor

Massachusetts Institute of Technology

United States

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Duane Boning is a Professor at the Massachusetts Institute of Technology, USA. His research focuses on advanced simulations and analysis within the fields of integrated photonics and microelectronics, as evidenced by his recent articles on topics such as dynamic convolutional neural operators, reliability testing, and Bayesian optimization. He is recognized for his work on device reliability and the performance modeling of silicon photonic devices.

Articles (18)

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2025

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2022

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2022

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2021

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2021

Collaborators (3)

Luca Daniel

Massachusetts Institute of Technology

UNITED STATES

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Uttara Chakraborty

Massachusetts Institute of Technology

UNITED STATES

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Carlos Andres Rios Ocampo

Assistant Professor

University of Maryland at College Park

UNITED STATES

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Duane Boning

Research Interests

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About

Articles (18)

PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation

SPIPE: Differentiable SPICE-Level Co-Simulation Program for Integrated Photonics and Electronics

Bound-Constrained Expectation Maximization for Weibull Competing-Risks Device Reliability

Gradient-Based Power Efficient Functional Synthesis for Programmable Photonic Circuits

Provable Routing Analysis of Programmable Photonic Circuits

Selecting robust silicon photonic designs after Bayesian optimization without extra simulations

Modern Trends in Microelectronics Packaging Reliability Testing

Inference of process variations in silicon photonics from characterization measurements

Few-Shot Bayesian Performance Modeling for Silicon Photonic Devices Under Process Variation

Impact of process variations on splitter-tree-based integrated optical phased arrays

Identification of Multiple Failure Mechanisms for Device Reliability Using Differential Evolution

Automatic Synthesis of Broadband Silicon Photonic Devices via Bayesian Optimization

Intelligent Optimization of Dosing Uniformity in Ion Implantation Systems

Understanding and Improving Virtual Metrology Systems Using Bayesian Methods

Modeling and Optimizing the Impact of Process and Equipment Parameters in Sputtering Deposition Systems Using a Gaussian Process Machine Learning Framework

Variation-Aware Methods and Models for Silicon Photonic Design-for-Manufacturability

Enabling Wavelength-Dependent Adjoint-Based Methods for Process Variation Sensitivity Analysis in Silicon Photonics

Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network

Collaborators (3)

Luca Daniel

Uttara Chakraborty

Carlos Andres Rios Ocampo

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