Nano-Electronic Simulation Software (NESS): A Novel Open-Source TCAD Simulation Environment

Medina-Bailón, Cristina; Dutta, T. K.; Adamu-Lema, Fikru; Rezaei, Ali; Nagy, Daniel; Georgiev, Vihar P.; Asenov, A.

doi:10.33079/jomm.20030404

Cited by 7 publications

(9 citation statements)

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“…24,38,43,80,81 Another problem that is often faced is the time and computational complexity in the stage of dataset generation, preprocessing, predictive analysis. To this view, one possible way is to develop a self-consistent software like Nano-Electronic Simulation Software, 82,83 and so forth. Moreover, there is still a gap between predicting the circuit level analysis of a device, which this work targets to bridge.…”

Section: Machine Learning Algorithm and Comparative Analysismentioning

confidence: 99%

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

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Section: Machine Learning Algorithm and Comparative Analysismentioning

confidence: 99%

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

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“…NESS [2,3,21,22] is a user-friendly TCAD semiconductor device simulator, under development by the Device Modelling group at the University of Glasgow. It has been developed considering three main concepts: (1) NESS enables simulations with increasing complexity and physical content within a unified environment (i.e., it offers the possibility of investigating a particular transistor structure with different complexity of simulations techniques from classical to quantum transport); (2) NESS has been designed to be flexible, easy to use, and extendable thanks to its modular structure; and (3) this modular structure allows for future collaborations and co-developments by industry and academia all over the world.…”

Section: Overview Of Nessmentioning

confidence: 99%

“…Fourthly, different transport simulation solvers [2,3,21] have been implemented in NESS to simulate the mobility, the charge density, and the current in nano-CMOS devices. All modules have been implemented with a high degree of parallelism making use of MPI and OpenMP libraries.…”

Section: Overview Of Nessmentioning

confidence: 99%

“…Many commercial and academic research groups are involved in the development of TCAD toolboxes and computational frameworks. In previous publications [2,3], we have presented in detail the inner workings of the user-friendly TCAD semiconductor device simulation platform called Nano-Electronic Simulation Software (NESS). In this paper, we focus on the illustration of the capabilities of NESS to simulate different device types and architectures.…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

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Simulation and Modeling of Novel Electronic Device Architectures with NESS (Nano-Electronic Simulation Software): A Modular Nano TCAD Simulation Framework

et al. 2021

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The modeling of nano-electronic devices is a cost-effective approach for optimizing the semiconductor device performance and for guiding the fabrication technology. In this paper, we present the capabilities of the new flexible multi-scale nano TCAD simulation software called Nano-Electronic Simulation Software (NESS). NESS is designed to study the charge transport in contemporary and novel ultra-scaled semiconductor devices. In order to simulate the charge transport in such ultra-scaled devices with complex architectures and design, we have developed numerous simulation modules based on various simulation approaches. Currently, NESS contains a drift-diffusion, Kubo–Greenwood, and non-equilibrium Green’s function (NEGF) modules. All modules are numerical solvers which are implemented in the C++ programming language, and all of them are linked and solved self-consistently with the Poisson equation. Here, we have deployed some of those modules to showcase the capabilities of NESS to simulate advanced nano-scale semiconductor devices. The devices simulated in this paper are chosen to represent the current state-of-the-art and future technologies where quantum mechanical effects play an important role. Our examples include ultra-scaled nanowire transistors, tunnel transistors, resonant tunneling diodes, and negative capacitance transistors. Our results show that NESS is a robust, fast, and reliable simulation platform which can accurately predict and describe the underlying physics in novel ultra-scaled electronic devices.

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“…The simulation results that we present in this paper are doubly tested by comparing them with independent series of data obtained from two different sources. One source is the previous S/D tunneling implementation inside the MS-EMC tool [ 1 , 2 ], and the other is the modular and open-source TCAD semiconductor device simulator called Nano-Electronic Simulation Software (NESS) [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Consistent Enhanced S/D Tunneling Implementation in a 2D MS-EMC Nanodevice Simulator

et al. 2021

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The implementation of a source to drain tunneling in ultrascaled devices using MS-EMC has traditionally led to overestimated current levels in the subthreshold regime. In order to correct this issue and enhance the capabilities of this type of simulator, we discuss in this paper two alternative and self-consistent solutions focusing on different parts of the simulation flow. The first solution reformulates the tunneling probability computation by modulating the WKB approximation in a suitable way. The second corresponds to a change in the current calculation technique based on the utilization of the Landauer formalism. The results from both solutions are compared and contrasted to NEGF results from NESS. We conclude that the current computation modification constitutes the most suitable and advisable strategy to improve the MS-EMC tool.

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Nano-Electronic Simulation Software (NESS): A Novel Open-Source TCAD Simulation Environment

Cited by 7 publications

References 0 publications

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Simulation and Modeling of Novel Electronic Device Architectures with NESS (Nano-Electronic Simulation Software): A Modular Nano TCAD Simulation Framework

Self-Consistent Enhanced S/D Tunneling Implementation in a 2D MS-EMC Nanodevice Simulator

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