First-Principles Study for Evidence of Low Interface Defect Density at Ge/GeO2_2 Interfaces

We present the evidence of the low defect density at Ge/GeO$_2$ interfaces in terms of first-principles total energy calculations. The energy advantages of the atom emission from the Ge/GeO$_2$ interface to release the stress due to the lattice mismatch are compared with those from the Si/SiO$_2$ interface. The energy advantages of the Ge/GeO$_2$ are found to be smaller than those of the Si/SiO$_2$ because of the high flexibility of the bonding networks in GeO$_2$. Thus, the suppression of the Ge-atom emission during the oxidation process leads to the improved electrical properties of the Ge/GeO$_2$ interfaces

Ab Initio Molecular-Dynamics Simulation of Neuromorphic Computing in Phase-Change Memory Materials.

We present an in silico study of the neuromorphic-computing behavior of the prototypical phase-change material, Ge2Sb2Te5, using ab initio molecular-dynamics simulations. Stepwise changes in structural order in response to temperature pulses of varying length and duration are observed, and a good reproduction of the spike-timing-dependent plasticity observed in nanoelectronic synapses is demonstrated. Short above-melting pulses lead to instantaneous loss of structural and chemical order, followed by delayed partial recovery upon structural relaxation. We also investigate the link between structural order and electrical and optical properties. These results pave the way toward a first-principles understanding of phase-change physics beyond binary switching.J.M.S. gratefully acknowledges funding from an internal graduate studentship provided by Trinity College, Cambridge, and from a U.K. Engineering and Physical Sciences Research Council Programme Grant (Grant No. EP/K004956/1). This work was primarily carried out using the Cambridge HPC facility (www.hpc.cam.ac.uk), and some additional calculations were performed using the ARCHER supercomputer through membership of the U.K. HPC Materials Chemistry Consortium, which is funded by EPSRC Grant No. EP/L000202.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acsami.5b0182

Germanium for high performance MOSFETs and optical interconnects

It is believed that to continue the scaling of silicon CMOS innovative device structures and new materials have to be created in order to continue the historic progress in information processing and transmission. Recently germanium has emerged as a viable candidate to augment Si for CMOS and optoelectronic applications. In this work we will first review recent results on growth of thin and thick films of Ge on Si, technology for appropriate cleaning of Ge, surface passivation using high-κ dielectrics, and metal induced crystallization of amorphous Ge and dopant activation. Next we will review application of Ge for high performance MOSFETs. Innovative Si/Ge MOS heterostructures will be described with high on current and low off currents. Finally we will describe optical detectors and modulators for on-chip and off-chip interconnect. Successful integration of Ge on Si should allow continued scaling of silicon CMOS to below 22 nm node. ©The Electrochemical Society

Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge

Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. Low ON-state resistance of filamentary RRAM devices further increases the energy consumption due to high-current read and write operations, and limits the array size and parallel multiply & accumulate operations. High-forming voltages needed for filamentary RRAM are not compatible with advanced CMOS technology nodes. To address all these challenges, we developed a forming-free and bulk switching RRAM technology based on a trilayer metal-oxide stack. We systematically engineered a trilayer metal-oxide RRAM stack and investigated the switching characteristics of RRAM devices with varying thicknesses and oxygen vacancy distributions across the trilayer to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching operation at megaohm regime with high current nonlinearity and programmed up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform based on trilayer bulk RRAM crossbars by combining energy-efficient switched-capacitor voltage sensing circuits with differential encoding of weights to experimentally demonstrate high-accuracy matrix-vector multiplication. We showcased the computational capability of bulk RRAM crossbars by implementing a spiking neural network model for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints

A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems

The human brain intrinsically operates with a large number of synapses, more than 10(15). Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for which the crossbar architecture containing an artificial synaptic node at each cross is indispensable. However, crossbar arrays suffer from the undesired leakage of signals through neighboring cells, which is a major challenge for implementing ANNs. In this work, we show that this challenge can be overcome by using Pt/TaOy/nanoporous (NP) TaOx/Ta memristor synapses because of their self-rectifying behavior, which is capable of suppressing unwanted leakage pathways. Moreover, our synaptic device exhibits high non-linearity (up to 10(4)), low synapse coupling (S.C, up to 4.00 x 10(-5)), acceptable endurance (5000 cycles at 85 degrees C), sweeping (1000 sweeps), retention stability and acceptable cell uniformity. We also demonstrated essential synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and spiking-timing-dependent plasticity (STDP), and simulated the recognition accuracy depending on the S.C for MNIST handwritten digit images. Based on the average S.C (1.60 x 10(-4)) in the fabricated crossbar array, we confirmed that our memristive synapse was able to achieve an 89.08% recognition accuracy after only 15 training epochs

1 μm gate length, In_0.75Ga_0.25As channel, thin body n-MOSFET on InP substrate with transconductance of 737μS/μm

The first demonstration of implant-free, flatband-mode In<sub>0.75</sub>Ga<sub>0.25</sub>As channel n-MOSFETs is reported. These 1 μm gate length MOSFETs, fabricated on a structure with average mobility of 7720 cm<sup>2</sup>/Vs and sheet carrier concentration of 3.3×10<sup>12</sup> cm<sup>-22</sup>, utilise a Pt gate, a high-k dielectric (k≈20), and a δ-doped InAlAs/InGaAs/InAlAs heterostructure. The devices have a typical maximum drive current (I<sub>d,sat</sub>) of 933 μA/μm, extrinsic transconductance (g<sub>m</sub>) of 737 μS/μm, gate leakage (I<sub>g</sub>) of 40 pA, and on-resistance (R<sub>on</sub>) of 555 Ωμm. The g<sub>m</sub> and R<sub>on</sub> figures of merit are the best reported to date for any III-V MOSFET

Regenerative memory in time-delayed neuromorphic photonic resonators

We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection. We disclose the existence of a unique temporal response characteristic of localized structures enabling an ideal support for bits in an optical buffer memory for storage and reshaping of data information. We link our experimental implementation, based upon a nanoscale nonlinear resonant tunneling diode driving a laser, to the paradigm of neuronal activity, the FitzHugh-Nagumo model with delayed feedback. This proof-of-concept photonic regenerative memory might constitute a building block for a new class of neuron-inspired photonic memories that can handle high bit-rate optical signals

Oxidation behavior of graphene-coated copper at intrinsic graphene defects of different origins

The development of ultrathin barrier films is vital to the advanced semiconductor industry. Graphene appears to hold promise as a protective coating; however, the polycrystalline and defective nature of engineered graphene hinders its practical applications. Here, we investigate the oxidation behavior of graphene-coated Cu foils at intrinsic graphene defects of different origins. Macro-scale information regarding the spatial distribution and oxidation resistance of various graphene defects is readily obtained using optical and electron microscopies after the hot-plate annealing. The controlled oxidation experiments reveal that the degree of structural deficiency is strongly dependent on the origins of the structural defects, the crystallographic orientations of the underlying Cu grains, the growth conditions of graphene, and the kinetics of the graphene growth. The obtained experimental and theoretical results show that oxygen radicals, decomposed from water molecules in ambient air, are effectively inverted at Stone-Wales defects into the graphene/Cu interface with the assistance of facilitators