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Charon User Manual: v. 2.2 (revision1)

Musson, Lawrence M.; Hennigan, Gary L.; Gao, Xujiao G.; Humphreys, Richard H.; Negoita, Mihai N.; Huang, Andy H.

This manual gives usage information for the Charon semiconductor device simulator. Charon was developed to meet the modeling needs of Sandia National Laboratories and to improve on the capabilities of the commercial TCAD simulators; in particular, the additional capabilities are running very large simulations on parallel computers and modeling displacement damage and other radiation effects in significant detail. The parallel capabilities are based around the MPI interface which allows the code to be ported to a large number of parallel systems, including linux clusters and proprietary “big iron” systems found at the national laboratories and in large industrial settings.

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Revealing quantum effects in highly conductive δ-layer systems

Communications Physics

Mamaluy, Denis M.; Mendez Granado, Juan P.; Gao, Xujiao G.; Misra, Shashank M.

Thin, high-density layers of dopants in semiconductors, known as δ-layer systems, have recently attracted attention as a platform for exploration of the future quantum and classical computing when patterned in plane with atomic precision. However, there are many aspects of the conductive properties of these systems that are still unknown. Here we present an open-system quantum transport treatment to investigate the local density of electron states and the conductive properties of the δ-layer systems. A successful application of this treatment to phosphorous δ-layer in silicon both explains the origin of recently-observed shallow sub-bands and reproduces the sheet resistance values measured by different experimental groups. Further analysis reveals two main quantum-mechanical effects: 1) the existence of spatially distinct layers of free electrons with different average energies; 2) significant dependence of sheet resistance on the δ-layer thickness for a fixed sheet charge density.

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An X-ray Intensity Operations Monitor (AXIOM) (Final LDRD Project Report)

Ulmen, Benjamin A.; Webb, Timothy J.; Radtke, Gregg A.; Olson, Aaron J.; Depriest, Kendall D.; Coffey, Sean K.; Looker, Quinn M.; Gao, Xujiao G.; Nicholas, Ryder N.; Edwards, Jarrod D.; McCourt, Andrew L.; Bell, Kate S.

The Saturn accelerator has historically lacked the capability to measure time-resolved spectra for its 3-ring bremsstrahlung x-ray source. This project aimed to create a spectrometer called AXIOM to provide this capability. The project had three major development pillars: hardware, simulation, and unfold code. The hardware consists of a ring of 24 detectors around an existing x-ray pinhole camera. The diagnostic was fielded on two shots at Saturn and over 100 shots at the TriMeV accelerator at Idaho Accelerator Center. A new Saturn x-ray environment simulation was created using measured data to validate. This simulation allows for timeresolved spectra computation to compare the experimental results. The AXIOM-Unfold code is a new parametric unfold code using modern global optimizers and uncertainty quantification. The code was written in Python, uses Gitlab version control and issue tracking, and has been developed with long term code support and maintenance in mind.

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Quantum Transport Simulations for Si:P δ-layer Tunnel Junctions

International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

Mendez Granado, Juan P.; Mamaluy, Denis M.; Gao, Xujiao G.; Misra, Shashank M.

We present an efficient self-consistent implementation of the Non-Equilibrium Green Function formalism, based on the Contact Block Reduction method for fast numerical efficiency, and the predictor-corrector approach, together with the Anderson mixing scheme, for the self-consistent solution of the Poisson and Schrödinger equations. Then, we apply this quantum transport framework to investigate 2D horizontal Si:P δ-layer Tunnel Junctions. We find that the potential barrier height varies with the tunnel gap width and the applied bias and that the sign of a single charge impurity in the tunnel gap plays an important role in the electrical current.

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Modeling and Assessment of Atomic Precision Advanced Manufacturing (APAM) Enabled Vertical Tunneling Field Effect Transistor

International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

Gao, Xujiao G.; Mendez Granado, Juan P.; Lu, Tzu-Ming L.; Anderson, Evan M.; Campbell, DeAnna M.; Ivie, Jeffrey A.; Schmucker, Scott W.; Grine, Albert D.; Lu, Ping L.; Tracy, Lisa A.; Arghavani, Reza A.; Misra, Shashank M.

The atomic precision advanced manufacturing (APAM) enabled vertical tunneling field effect transistor (TFET) presents a new opportunity in microelectronics thanks to the use of ultra-high doping and atomically abrupt doping profiles. We present modeling and assessment of the APAM TFET using TCAD Charon simulation. First, we show, through a combination of simulation and experiment, that we can achieve good control of the gated channel on top of a phosphorus layer made using APAM, an essential part of the APAM TFET. Then, we present simulation results of a preliminary APAM TFET that predict transistor-like current-voltage response despite low device performance caused by using large geometry dimensions. Future device simulations will be needed to optimize geometry and doping to guide device design for achieving superior device performance.

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FAIR DEAL Grand Challenge Overview

Allemang, Christopher R.; Anderson, Evan M.; Baczewski, Andrew D.; Bussmann, Ezra B.; Butera, Robert E.; Campbell, DeAnna M.; Campbell, Quinn C.; Carr, Stephen M.; Frederick, Esther F.; Gamache, Phillip G.; Gao, Xujiao G.; Grine, Albert D.; Gunter, Mathew M.; Halsey, Connor H.; Ivie, Jeffrey A.; Katzenmeyer, Aaron M.; Leenheer, Andrew J.; Lepkowski, William L.; Lu, Tzu-Ming L.; Mamaluy, Denis M.; Mendez Granado, Juan P.; Pena, Luis F.; Schmucker, Scott W.; Scrymgeour, David S.; Tracy, Lisa A.; Wang, George T.; Ward, Dan W.; Young, Steve M.

While it is likely practically a bad idea to shrink a transistor to the size of an atom, there is no arguing that it would be fantastic to have atomic-scale control over every aspect of a transistor – a kind of crystal ball to understand and evaluate new ideas. This project showed that it was possible to take a niche technique used to place dopants in silicon with atomic precision and apply it broadly to study opportunities and limitations in microelectronics. In addition, it laid the foundation to attaining atomic-scale control in semiconductor manufacturing more broadly.

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Simulation and Modeling of Time-Resolved X-Ray Detector for the Saturn Accelerator

IEEE Transactions on Nuclear Science

Gao, Xujiao G.; Looker, Quinn M.; Webb, Timothy J.; Depriest, Kendall D.; Ulmen, Benjamin A.

We present the technology-aided computer design (TCAD) device simulation and modeling of a silicon p-i-n diode for detecting time-dependent X-ray radiation. We show that the simulated forward and reverse breakdown current-voltage characteristics agree well with the measured data under nonradiation environment by only calibrating carrier lifetimes for the forward bias case and avalanche model critical fields for the reverse bias condition. Using the calibrated parameters and other nominal material properties, we simulated the radiation responses of the p-i-n diode and compared with experimental data when the diode was exposed to X-ray radiation at Sandia's Saturn facility and the Idaho State University (ISU) TriMeV facility. For Saturn's Gaussian dose-rate pulses, we show three findings from TCAD simulations. First, the simulated photocurrents are in excellent agreement with the measured data for two dose-rate pulses with peak values of 1.16 times 10 -{10} and 1.88 times 10 -{10} rad(Si)/s. Second, the simulation results of high dose-rate pulses predict increased delayed photocurrents with longer time tails in the diode electrical responses due to excess carrier generation. Third, simulated peak values of diode radiation responses versus peak dose rates at different bias conditions provide useful guidance to determine the dose-rate range that the p-i-n diode can reliably detect in experiment. For TriMeV's non-Gaussian dose-rate pulse, our simulated diode response is in decent agreement with the measured data without further calibration. We also studied the effects of device geometry, recombination process, and dose-rate enhancement via TCAD simulations to understand the higher measured response in the time after the peak dose-rate radiation for the p-i-n diode exposed to TriMeV irradiation.

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Modeling assisted room temperature operation of atomic precision advanced manufacturing devices

International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

Gao, Xujiao G.; Tracy, Lisa A.; Anderson, Evan M.; Campbell, DeAnna M.; Ivie, Jeffrey A.; Lu, Tzu-Ming L.; Mamaluy, Denis M.; Schmucker, Scott W.; Misra, Shashank M.

One big challenge of the emerging atomic precision advanced manufacturing (APAM) technology for microelectronics application is to realize APAM devices that operate at room temperature (RT). We demonstrate that semiclassical technology computer aided design (TCAD) device simulation tool can be employed to understand current leakage and improve APAM device design for RT operation. To establish the applicability of semiclassical simulation, we first show that a semiclassical impurity scattering model with the Fermi-Dirac statistics can explain the very low mobility in APAM devices quite well; we also show semiclassical TCAD reproduces measured sheet resistances when proper mobility values are used. We then apply semiclassical TCAD to simulate current leakage in realistic APAM wires. With insights from modeling, we were able to improve device design, fabricate Hall bars, and demonstrate RT operation for the very first time.

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Physics-informed graph neural network for circuit compact model development

International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

Gao, Xujiao G.; Huang, Andy H.; Trask, Nathaniel A.; Reza, Shahed R.

We present a Physics-Informed Graph Neural Network (pigNN) methodology for rapid and automated compact model development. It brings together the inherent strengths of data-driven machine learning, high-fidelity physics in TCAD simulations, and knowledge contained in existing compact models. In this work, we focus on developing a neural network (NN) based compact model for a non-ideal PN diode that represents one nonlinear edge in a pigNN graph. This model accurately captures the smooth transition between the exponential and quasi-linear response regions. By learning voltage dependent non-ideality factor using NN and employing an inverse response function in the NN loss function, the model also accurately captures the voltage dependent recombination effect. This NN compact model serves as basis model for a PN diode that can be a single device or represent an isolated diode in a complex device determined by topological data analysis (TDA) methods. The pigNN methodology is also applicable to derive reduced order models in other engineering areas.

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Results 1–25 of 90
Results 1–25 of 90