Publications

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The precise positioning of dopants in semiconductors using scanning tunneling microscopes has led to the development of planar dopant-based devices, also known as δ layer-based devices, facilitating the exploration of new concepts in classical and quantum computing. Recently, it has been shown that two distinct conductivity regimes (low- and high-bias regimes) exist in δ-layer tunnel junctions due to the presence of quasi-discrete and continuous states in the conduction band of δ-layer systems. Furthermore, discrete charged impurities in the tunnel junction region significantly influence the tunneling rates in δ-layer tunnel junctions. Here we demonstrate that electrical dipoles, i.e. zero-charge defects, present in the tunnel junction region can also significantly alter the tunneling rate, depending, however, on the specific conductivity regime, and orientation and moment of the dipole. In the low-bias regime, with high-resistance tunneling mode, dipoles of nearly all orientations and moments can alter the current, indicating the extreme sensitivity of the tunneling current to the slightest imperfection in the tunnel gap. In the high-bias regime, with low-resistivity, only dipoles with high moments and oriented in the directions perpendicular to the electron tunneling direction can significantly affect the current, thus making this conductivity regime significantly less prone to the influence of dipole defects with low-moments or oriented in the direction parallel to the tunneling.

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This project uses a quantum simulation technique to reveal the true conducting properties of novel atomic precision advanced manufacturing materials. With Moore's law approaching the limit of scaling for the CMOS technology, it is crucial to provide the best computing power and resources to National Security missions. Atomic precision advanced manufacturing-based computing systems can become the key to the design, use, and security of modern weapon systems, critical infrastructure, and communications. We will utilize the state-of-the-art computational methodology to create a predictive simulator for p-type atomic precision advanced manufacturing systems, which may also find applications in counterfeit detection and anti-tamper.

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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.

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.

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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We employ a fully charge self-consistent quantum transport formalism, together with a heuristic elastic scattering model, to study the local density of state (LDOS) and the conductive properties of Si:P δ-layer wires at the cryogenic temperature of 4 K. The simulations allow us to explain the origin of shallow conducting sub-bands, recently observed in high resolution angle-resolved photoemission spectroscopy experiments. Our LDOS analysis shows the free electrons are spatially separated in layers with different average kinetic energies, which, along with elastic scattering, must be accounted for to reproduce the sheet resistance values obtained over a wide range of the δ-layer donor densities.

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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This report provides an overview of a workshop held on July 27-28, 2016 at Sandia National Laboratories in Albuquerque to itemize the DOE laboratory capabilties and provide a high level organization of those capabilties into a full evaluation framework for new computing paradigms that spans from fundamental breakthroughs in materials and devices to full system architectures and software environments.

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Transition metal oxide (TMO) memristors have recently attracted special attention from the semiconductor industry and academia. Memristors are one of the strongest candidates to replace flash memory, and possibly DRAM and SRAM in the near future. Moreover, memristors have a high potential to enable beyond-CMOS technology advances in novel architectures for high performance computing (HPC). The utility of memristors has been demonstrated in reprogrammable logic (cross-bar switches), brain-inspired computing and in non-CMOS complementary logic. Indeed, the potential use of memristors as logic devices is especially important considering the inevitable end of CMOS technology scaling that is anticipated by 2025. In order to aid the on-going Sandia memristor fabrication effort with a memristor design tool and establish a clear physical picture of resistance switching in TMO memristors, we have created and validated with experimental data a simulation tool we name the Memristor Charge Transport (MCT) Simulator.

We present a study of the 'snap-back' regime of resistive switching hysteresis in bipolar TaOx memristors, identifying power signatures in the electronic transport. Using a simple model based on the thermal and electric field acceleration of ionic mobilities, we provide evidence that the 'snap-back' transition represents a crossover from a coupled thermal and electric-field regime to a primarily thermal regime, and is dictated by the reconnection of a ruptured conducting filament. We discuss how these power signatures can be used to limit filament radius growth, which is important for operational properties such as power, speed, and retention.

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As CMOS technology approaches the end of its scaling, oxide-based memristors have become one of the leading candidates for post-CMOS memory and logic devices. To facilitate the understanding of physical switching mechanisms and accelerate experimental development of memristors, we have developed a three-dimensional fully-coupled electrical and thermal transport model, which captures all the important processes that drive memristive switching and is applicable for simulating a wide range of memristors. The model is applied to simulate the RESET and SET switching in a 3D filamentary TaOx memristor. Extensive simulations show that the switching dynamics of the bipolar device is determined by thermally-activated field-dominant processes: with Joule heating, the raised temperature enables the movement of oxygen vacancies, and the field drift dominates the overall motion of vacancies. Simulated current-voltage hysteresis and device resistance profiles as a function of time and voltage during RESET and SET switching show good agreement with experimental measurement.

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In this paper, we present a fully-coupled electrical and thermal transport model for oxide memristors that solves simultaneously the time-dependent continuity equations for all relevant carriers, together with the time-dependent heat equation including Joule heating sources. The model captures all the important processes that drive memristive switching and is applicable to simulate switching behavior in a wide range of oxide memristors. The model is applied to simulate the ON switching in a 3D filamentary TaOx memristor. Simulation results show that, for uniform vacancy density in the OFF state, vacancies fill in the conduction filament till saturation, and then fill out a gap formed in the Ta electrode during ON switching; furthermore, ON-switching time strongly depends on applied voltage and the ON-to-OFF current ratio is sensitive to the filament vacancy density in the OFF state.

Two topics funded under Sandia Laboratory's Lab-Directed Research and Development funds are described.

We predict that within next 15 years a fundamental down-scaling limit for CMOS technology and other Field-Effect Transistors (FETs) will be reached. Specifically, we show that at room temperatures all FETs, irrespective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths. These findings were confirmed by performing quantum mechanical transport simulations for a variety of 6-, 5-, and 4-nm gate length Si devices, optimized to satisfy high-performance logic specifications by ITRS. Different channel materials and wafer/channel orientations have also been studied; it is found that altering channel-source-drain materials achieves only insignificant increase in switching energy, which overall cannot sufficiently delay the approaching downscaling limit. Alternative possibilities are discussed to continue the increase of logic element densities for room temperature operation below the said limit.

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We created a highly efficient, universal 3D quant um transport simulator. We demonstrated that the simulator scales linearly - both with the problem size (N) and number of CPUs, which presents an important break-through in the field of computational nanoelectronics. It allowed us, for the first time, to accurately simulate and optim ize a large number of realistic nanodevices in a much shorter time, when compared to other methods/codes such as RGF[%7EN 2.333 ]/KNIT, KWANT, and QTBM[%7EN 3 ]/NEMO5. In order to determine the best-in-class for different beyond-CMOS paradigms, we performed rigorous device optimization for high-performance logic devices at 6-, 5- and 4-nm gate lengths. We have discovered that there exists a fundamental down-scaling limit for CMOS technology and other Field-Effect Transistors (FETs). We have found that, at room temperatures, all FETs, irre spective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths.

This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

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