Flash memory devices--that is, non-volatile computer storage media that can be electrically erased and reprogrammed--are vital for portable electronics, but the scaling down of metal-oxide-semiconductor (MOS) flash memory to sizes of below ten nanometres per data cell presents challenges. Molecules have been proposed to replace MOS flash memory, but they suffer from low electrical conductivity, high resistance, low device yield, and finite thermal stability, limiting their integration into current MOS technologies. Although great advances have been made in the pursuit of molecule-based flash memory, there are a number of significant barriers to the realization of devices using conventional MOS technologies. Here we show that core-shell polyoxometalate (POM) molecules can act as candidate storage nodes for MOS flash memory. Realistic, industry-standard device simulations validate our approach at the nanometre scale, where the device performance is determined mainly by the number of molecules in the storage media and not by their position. To exploit the nature of the core-shell POM clusters, we show, at both the molecular and device level, that embedding [(Se(IV)O3)2](4-) as an oxidizable dopant in the cluster core allows the oxidation of the molecule to a [Se(v)2O6](2-) moiety containing a {Se(V)-Se(V)} bond (where curly brackets indicate a moiety, not a molecule) and reveals a new 5+ oxidation state for selenium. This new oxidation state can be observed at the device level, resulting in a new type of memory, which we call 'write-once-erase'. Taken together, these results show that POMs have the potential to be used as a realistic nanoscale flash memory. Also, the configuration of the doped POM core may lead to new types of electrical behaviour. This work suggests a route to the practical integration of configurable molecules in MOS technologies as the lithographic scales approach the molecular limit.

We explore the concept that the incorporation of polyoxometalates (POMs) into complementary metal oxide semiconductor (CMOS) technologies could offer a fundamentally better way to design and engineer new types of data storage devices, due to the enhanced electronic complementarity with SiO2, high redox potentials, and multiple redox states accessible to polyoxometalate clusters. To explore this we constructed a custom-built simulation domain bridge. Connecting DFT, for the quantum mechanical modelling part, and mesoscopic device modelling, confirms the theoretical basis for the proposed advantages of POMs in non-volatile molecular memories (NVMM) or flash-RAM.

In the field of molecular electronics, an intimate link between the delocalization of molecular orbitals and their ability to support current flow is often assumed. Delocalization, in turn, is generally regarded as being synonymous with structural symmetry, for example, in the lengths of the bonds along a molecular wire. In this work, we use density functional theory in combination with nonequilibrium Green's functions to show that precisely the opposite is true in the extended metal atom chain Cr(3)(dpa)(4)(NCS)(2) where the delocalized π framework has previously been proposed to be the dominant conduction pathway. Low-symmetry distortions of the Cr(3) core do indeed reduce the effectiveness of these π channels, but this is largely irrelevant to electron transport at low bias simply because they lie far below the Fermi level. Instead, the dominant pathway is through higher-lying orbitals of σ symmetry, which remain essentially unperturbed by even quite substantial distortions. In fact, the conductance is actually increased marginally because the σ(nb) channel is displaced upward toward the Fermi level. These calculations indicate a subtle and counterintuitive relationship between structure and function in these metal chains that has important implications for the interpretation of data emerging from scanning tunnelling and atomic force microscopy experiments.

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This work investigates the possibility to replace numerical TCAD device simulations with a multi-layer neural network (NN). We explore if it is possible to train the NN with the required accuracy in order to predict device characteristics of thousands of transistors without executing TCAD simulations. In order to answer this question, here we present a hierarchical multi-scale simulation study of a silicon junctionless nanowire field-effect transistor (JL-NWT) with a gate length of 150 nm and diameter of an Si channel of 8 nm. All device simulations are based on the Drift-Diffusion (DD) formalism with activated density gradient (DG) quantum corrections. For the purpose of this work, we perform statistical numerical experiments of a set of 1380 automictically different JL-NWTs. Each device has a unique random distribution of discrete dopants (RDD) within the silicon body. From those statistical simulations, we extract important figures of merit (FoM), such as OFF-current (IOFF) and ONcurrent (ION), subthreshold slope (SS) and voltage threshold (VTH). Based on those statistical simulations, we train a multi-layer NN and we compare the obtained results with a general linear model (GLM). Our work shows the potential of using NN in the field of device modelling and simulation with a potential application to significantly reduce the computational cost.

Polyoxometalates (POMs) are promising candidates for molecular electronic applications because (1) they are inorganic molecules, which have better CMOS compatibility compared to organic molecules; (2) they are easily synthesized in a one-pot reaction from metal oxides (MO x ) (where the metal M can be, e.g., W, V, or Mo, and x is an integer between 4 and 7); (3) POMs can self-assemble to form various shapes and configurations, and thus the chemical synthesis can be tailored for specific device performance; and (4) they are redox-active with multiple states that have a very low voltage switching between polarized states. However, a deep understanding is required if we are to make commercial molecular devices a reality. Simulation and modeling are the most time efficient and cost-effective methods to evaluate a potential device performance. Here, we use density functional theory in combination with nonequilibrium Green’s function to study the transport properties of [W 18 O 54 (SO 3 ) 2 ] 4– , a POM cluster, in a variety of molecular junction configurations. Our calculations reveal that the transport profile not only is linked to the electronic structure of the molecule but also is influenced by contact geometry and presence of ions. More specifically, the contact geometry and the number of bonds between the POM and the electrodes determine the current flow. Hence, strong and reproducible contact between the leads and the molecule is mandatory to establish a reliable fabrication process. Moreover, although often ignored, our simulations show that the charge balancing counterions activate the conductance channels intrinsic to the molecule, leading to a dramatic increase in the computed current at low bias. Therefore, the role of these counterions cannot be ignored when molecular based devices are fabricated. In summary, this work shows that the current transport in POM junctions is determined by not only the contact geometry between the molecule and the electrode but also the presence of ions around the molecule. This significantly impacts the transport properties in such nanoscale molecular electronic devices.

The aim of this paper is to present a flexible and open-source multi-scale simulation software which has been developed by the Device Modelling Group at the University of Glasgow to study the charge transport in contemporary ultra-scaled Nano-CMOS devices. The name of this new simulation environment is Nano-electronic Simulation Software (NESS). Overall NESS is designed to be flexible, easy to use and extendable. Its main two modules are the structure generator and the numerical solvers module. The structure generator creates the geometry of the devices, defines the materials in each region of the simulation domain and includes eventually sources of statistical variability. The charge transport models and corresponding equations are implemented within the numerical solvers module and solved self-consistently with Poisson equation. Currently, NESS contains a drift–diffusion, Kubo–Greenwood, and non-equilibrium Green’s function (NEGF) solvers. The NEGF solver is the most important transport solver in the current version of NESS. Therefore, this paper is primarily focused on the description of the NEGF methodology and theory. It also provides comparison with the rest of the transport solvers implemented in NESS. The NEGF module in NESS can solve transport problems in the ballistic limit or including electron–phonon scattering. It also contains the Flietner model to compute the band-to-band tunneling current in heterostructures with a direct band gap. Both the structure generator and solvers are linked in NESS to supporting modules such as effective mass extractor and materials database. Simulation results are outputted in text or vtk format in order to be easily visualized and analyzed using 2D and 3D plots. The ultimate goal is for NESS to become open-source, flexible and easy to use TCAD simulation environment which can be used by researchers in both academia and industry and will facilitate collaborative software development.

In this letter, we report a quantum transport simulation study of the impact of random discrete dopants (RDDs) on Si-InAs nanowire p-type Tunnel FETs. The bandto-band tunneling is simulated using the non-equilibrium Green's function formalism in effective mass approximation, implementing a two-band model of the imaginary dispersion. We have found that RDDs induce strong variability not only in the OFF-state but also in the ON-state current of the TFETs. Contrary to the nearly normal distribution of the RDD-induced ON-current variations in conventional CMOS transistors, the TFET's ON-currents variations are described by a logarithmic distribution. The distributions of other figures of merit (FoM) such as threshold voltage and subthreshold swing are also reported. The variability in the FoM is analyzed by studying the correlation between the number and the position of the dopants.