Articleshttps://imec-publications.be/handle/20.500.12860/52024-03-29T00:38:19Z2024-03-29T00:38:19ZAnisotropy and effective medium approach in the optical response of two-dimensional material heterostructuresMajerus, BrunoGuillaume, EmerickKockaert, PascalHenrard, Lucguillaumehttps://imec-publications.be/handle/20.500.12860/43469.22024-03-28T10:23:32Z2023-01-01T00:00:00ZAnisotropy and effective medium approach in the optical response of two-dimensional material heterostructures
Majerus, Bruno; Guillaume, Emerick; Kockaert, Pascal; Henrard, Luc; guillaume
Two-dimensional (2D) materials offer a large variety of optical properties, from transparency to plasmonic excitation. They can be structured and combined to form heterostructures that expand the realm of possibility to manipulate light interactions at the nanoscale. Appropriate and numerically efficient models accounting for the high intrinsic anisotropy of 2D materials and heterostructures are needed. In this article, we retrieve the relevant intrinsic parameters that describe the optical response of a homogeneous 2D material from a microscopic approach. Well-known effective models for vertical heterostructure (stacking of different layers) are retrieved. We found that the effective optical response model of horizontal heterostructures (alternating nanoribbons) depends on the thickness. In the thin layer model, well adapted for 2D materials, a counterintuitive in-plane isotropic behavior is predicted. We confront the effective model formulation with exact reference calculations such as ab initio calculations for graphene, hexagonal boron nitride (hBN), as well as corrugated graphene with larger thickness but also with classical electrodynamics calculations that exactly account for the lateral structuration.
2023-01-01T00:00:00ZAnalysis and modeling of reverse-biased gate leakage current in AlGaN/GaN high electron mobility transistorsRai, NarendraSarkar, RitamMahajan, AshutoshLaha, ApurbaSaha, DipankarGanguly, Swaroophttps://imec-publications.be/handle/20.500.12860/43412.22024-03-28T08:46:53Z2023-01-01T00:00:00ZAnalysis and modeling of reverse-biased gate leakage current in AlGaN/GaN high electron mobility transistors
Rai, Narendra; Sarkar, Ritam; Mahajan, Ashutosh; Laha, Apurba; Saha, Dipankar; Ganguly, Swaroop
We have analyzed and modeled the reverse-biased gate leakage current in a Schottky-gate AlGaN/GaN high electron mobility transistor. While the Poole-Frenkel emission current along conductive threading dislocations dominates at low negative gate bias, the trap-assisted tunneling of thermally energized electrons and the thermal emission of electrons from threading dislocations aided by dislocation-related states at multiple energy levels within the AlGaN bandgap are dominant at moderate to large reverse bias. Additionally, deep trap levels of high density localized near the gate/AlGaN interface cause significant leakage at 473 K at low to moderate reverse bias, which could be specific to the device we have analyzed. We extracted about 10(12) cm(-2) traps near the AlGaN/GaN interface from the difference of the barrier layer electric field profile obtained from the experimental high-frequency capacitance-gate voltage and the one needed for final matching. The thermionic- and the thermionic field-emission currents are considerably low; the latter, however, dominates in the defect-free case. Finally, the simulation framework we developed here helped us identify various conduction mechanisms contributing to the reverse-biased gate leakage and the density and electronic structure of the responsible defects.
2023-01-01T00:00:00ZAn artificial intelligence course for chemical engineersWu, MinDi Caprio, UldericoVermeire, FlorenceHellinckx, PeterBraeken, LeenWaldherr, SteffenLeblebici, M. Enishttps://imec-publications.be/handle/20.500.12860/43508.22024-03-28T08:40:32Z2023-01-01T00:00:00ZAn artificial intelligence course for chemical engineers
Wu, Min; Di Caprio, Ulderico; Vermeire, Florence; Hellinckx, Peter; Braeken, Leen; Waldherr, Steffen; Leblebici, M. Enis
Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum.
2023-01-01T00:00:00ZAlgorithmic Optimization of Transistors Applied to Silicon LDMOSChuang, Ping-JuSaadat, AliVan de Put, MaartenEdwards, HalVandenberghe, William G. G.https://imec-publications.be/handle/20.500.12860/42175.32024-03-28T08:26:15Z2023-01-01T00:00:00ZAlgorithmic Optimization of Transistors Applied to Silicon LDMOS
Chuang, Ping-Ju; Saadat, Ali; Van de Put, Maarten; Edwards, Hal; Vandenberghe, William G. G.
We propose a pioneering approach that integrates optimization algorithms and technology computer-aided design to automatically optimize laterally-diffused metal-oxide-semiconductors (LDMOS) with a field-oxide structure. We define the ratio of the square of the breakdown voltage divided by the specific on-resistance as the figure-of-merit (FOM) and the objective function of our optimization. We compare the performance of three different algorithms: Nelder-Mead, Powell, and Bayesian Optimization. We show how the LDMOS performance evolves as each of the three optimization algorithms reach their optimized structure. We show that a straightforward Nelder-Mead optimization leads to a local optimum when optimizing over six input parameters. We find that Bayesian Optimization is the most data-efficient method to find the global optimized structure in the multi-domain design space.
2023-01-01T00:00:00Z