Bulletin of the American Physical Society
APS March Meeting 2018
Volume 63, Number 1
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session K15: Post-Moore ComputingFocus
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Sponsoring Units: DMP Chair: Kaushik Roy, Purdue University Room: LACC 304C |
Wednesday, March 7, 2018 8:00AM - 8:36AM |
K15.00001: Stochastic Switching of Nanomagnets for Post-CMOS Computing Invited Speaker: Kaushik Roy Magnetization reversal in spintronic devices is stochastic and is characterized by time-varying thermal noise. Rather than viewing the device stochasticity as a disadvantage, the inherent probabilistic switching dynamics of a nanomagnet can be used to mimic the computational primitives of several Post-Moore computing paradigms. For instance, neuromorphic computing platforms enabled with stochastic neurons and synapses can be used to perform probabilistic inference in Restricted Boltzmann Machines and Deep Belief Network architectures. More generally, they can be used to implement Boltzmann machines enabled by stochastic units that can be used to find optimal solutions in combinatorial optimization problems. Direct mapping of the stochastic computational units of such probabilistic computing paradigms results in reduced energy and area overhead with respect to CMOS implementations (enabled by deterministic hardware). Further, due to stochastic state updates over time, such probabilistic computing paradigms offer the possibility of state compression of their units in comparison to their deterministic counterparts. Here, we report our recent work on using the stochastic magnetization dynamics of a Magnetic Tunnel Junction to enable various genres of post-Moore computing like Spiking Neural Networks, Boltzmann Machines and Bayesian Inference Networks. |
Wednesday, March 7, 2018 8:36AM - 9:12AM |
K15.00002: Gallium Nitride: Extreme Properties (and Opportunities) for Post-Moore Computing Invited Speaker: Tomas Palacios Gallium Nitride and its alloys with Aluminum and Indium have unique properties to impact both More-Moore and More-than-Moore electronics. This paper will first review how the combination of the wide bandgap of GaN, and its relatively low dielectric constant make GaN an ideal semiconductor for digital electronics below the 5 nm gate length node. In addition to be an excellent candidate for the next generation of digital electronics, GaN also enables completely novel semiconductor devices such as hot electron-transistors that can push electronics even further. Finally, the paper will also discuss different strategies to heterogeneously integrate GaN with silicon electronics. This integration brings new features to traditional silicon electronics, such as highly compact point-of-load power management, very efficient rf amplifiers, as well as integrated photonics. The unique combination of new devices and important system-level impact makes GaN an extreme material with the potential to revolutionize computing at many different levels. |
Wednesday, March 7, 2018 9:12AM - 9:24AM |
K15.00003: Proposal for Reconfigurable Magnetic Tunnel Diode and Transistor Ersoy Sasioglu, Stefan Bluegel, Ingrid Mertig We propose a reconfigurable magnetic tunnel diode and transistor using spin gapless semiconductors (SGSs) and half metallic magnets (HMMs) [1]. The two-terminal tunnel diode is comprised of a SGS electrode and a HMM electrode separated by a thin insulating (I) tunnel barrier. The tunnel diode allows electrical current to pass either in one direction or in other direction depending of the relative orientation of the magnetization direction of the electrodes. The three-terminal magnetic tunnel transistor has HMM-I-SGS-I-HMM (emitter-base-collector) structure and can be switched on and off by application of a voltage to the base electrode and conducts current in both directions. Both devices can be configured by the spin transfer torque switching mechanism. We demonstrate the reconfigurable rectification characteristics of the proposed diode based on two-dimensional transition-metal dichalcogenides by employing the nonequilibrium Green's function method combined with density functional theory. |
Wednesday, March 7, 2018 9:24AM - 9:36AM |
K15.00004: Reversible Fluxon Logic: Topological particles enable gates beyond the standard adiabatic limit Waltraut Wustmann, Kevin Osborn
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Wednesday, March 7, 2018 9:36AM - 9:48AM |
K15.00005: Effect of vacancy defects on the carrier transport of a two-dimensional Topological Insulator Field effect Transistor Sabyasachi Tiwari, Maarten Van de Put, William Vandenberghe As scaling reduces field-effect transistor (FET) channel length, optimal electrostatic control can be maintained by using two-dimensional (2D) materials as the channel material. However, 2D materials are marred with a high level of defects, which negatively affects their carrier transport properties. Interestingly, 2D topological insulators (TIs) are shown to be immune to imperfections as the topological edge states are protected by time reversal symmetry. However, a better understanding of the effect of the vacancy defects on the ballistic transport properties of the 2D topological insulators is necessary. We present a theoretical study of the effect of vacancy defects using a 2D Non-Equilibrium Green’s function (NEGF) based formulation with a Bernevig-Hughes-Zhang type tight-binding Hamiltonian, including spin-orbit interaction. We account for the open nature of the system by introducing appropriate contact self-energy terms using quantum transmitting boundary conditions. We analyze the ballistic transport characteristics of various TI-FETs and identify the topological edge symmetry breaking limit as a function of both position and amount of vacancy defects compared to the pristine TI lattice. |
Wednesday, March 7, 2018 9:48AM - 10:24AM |
K15.00006: Materials challenges for non-silicon matrix multipliers and neuromorphic computing Invited Speaker: Supratik Guha Workload driven changes in computing have had two major hardware consequences: (i), the emergence of new types of non-volatile memory; and (ii), dedicated hardware for executing neural network algorithms. Items (i) and (ii) are related, and new types of nanoscale memory and selector switch elements based upon phase change, magnetic, electrochemical, and insulator-to-metal (IMT) transition phenomena are being investigated as cross-bar memories, in-memory computing for matrix multipliers, and neuromorphic circuits that require devices mimicing synaptic and artificial neuron functions. I will first define—from the materials science perspective—performance characteristics of synaptic and neuronal devices that will need to be met to satisfy the needs for neuromorphic computing and the building of matrix multipliers. Using simple models based upon diffusion, surface tension and drift in conducting filament devices, I will examine the differences between selector switches and non-volatile memories and highlight the materials characteristics desirable for these two types of devices, and the limits on their variability. I will also describe our results in successfully building low voltage (<500 mV) artificial neurons using IMT materials, and low voltage conductive filament memory devices that use ultraporous matrices based upon a new synthesis technique called sequential-infiltration-system (SIS) [1]. |
Wednesday, March 7, 2018 10:24AM - 10:36AM |
K15.00007: Volatile resistive memory and dynamics of VOX nanodevices Javier Del Valle Granda, Pavel Salev, Juan Trastoy, Yoav Kalcheim, Ilya Valmianski, Juan Ramirez, Marcelo Rozenberg, Ivan Schuller VO2 and V2O3 are two classical examples of strongly electron-correlated materials, showing an Insulator-Metal transition (IMT) at 340 K and 160 K respectively. The possibility to induce the IMT using electric current or voltage has made them attractive for applications in emerging technologies such as RRAM memories or neuromorphic computing. We have studied the dynamics of the IMT in VOX nanodevices using fast voltage pulses. We found that above a threshold voltage (Vth), the IMT is induced within a few nanoseconds, after which the device returns to the insulating state in less than 10 ns. Interestingly, the system retains a “memory” of the event that persists much longer, during which it is possible to trigger the IMT with voltages well below Vth. This effect opens the possibility for a new kind of volatile memory, and it could have important implications for the implementation of IMT-based neuristors or RRAMs. |
Wednesday, March 7, 2018 10:36AM - 10:48AM |
K15.00008: "The role of dopants in the transition temperature of phase change material GeTe" Michelle Johannes, Noam Bernstein, Gabor Csanyi, Felix Mocanu, Volker Deringer Phase change material GeTe experiences a resistivity change on the order of a factor of 106 as it changes from amorphous to crystalline upon heating. This change is fast (on the scale of a nanosecond) and repeatable and makes this material a good candidate for high power electronic switching. We perform simulations to model phase change dichalcogenide material GeTe concentrating on the effects of nitrogen dopants on the melting and re-crystallization temperatures. We do this using Density Functional Theory (DFT), and ab-initio Molecular Dynamics (AIMD) to develop a machine-learning motivated Gaussian Approximation Potential. By employing a constant enthalpy simulation at various starting temperatures, we establish a liquid/crystal equilibrium phase that is by construction at the melting temperature and which is in good agreement with experiment. Subsequent calculations gauge the recrystallization temperature as a function of nitrogen dopant concentration with the goal of raising the phase change temperature without sacrificing resistivity change or introducing large volume changes. We explore the underlying mechanism by which the dopants impede recrystallization. |
Wednesday, March 7, 2018 10:48AM - 11:00AM |
K15.00009: Multi-resistance states and neural learning with perovskite nickelates Fan Zuo, Priyadarshini Panda, Kaushik Roy, Shriram Ramanathan Habituation behavior, which can be defined as decrement in response to repeated stimuli, is a fundamental form of non-associative learning observed among all living systems, from human beings with central nervous system to single-cell organism without a brain. We present realization of similar behavior in a quantum perovskite nickelate system via dynamical modulation of electron localization. This is achieved by weak anchoring of hydrogen to interstitial sites in the perovskite accompanied by charge transfer into the eg orbitals of Ni. The electron transfer from hydrogen creates a strongly correlated Ni2+ state that is insulating with a gap of nearly 3 eV for one electron/unit cell doping level. Since the dopant is weakly bound and quite mobile, we can create varying levels of volatility. Hence, a range of resistance states can be temporally controlled. This behavior can be modeled by exponential relaxations and the resulted plasticity can be incorporated into neural learning algorithms. We find that incorporating controlled memory loss enhances learning by forgetting non-critical information. The studies suggest that control of strong correlations in complex oxides presents an opportunity for materials design in the quest for post-Moore disruptive computing vectors. |
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