Electrical Properties of Graphene for Interconnect Applications

A semi-classical electrodynamical model is derived to describe the electrical transport along graphene, based on the modified Boltzmann transport equation. The model is derived in the typical operating conditions predicted for future integrated circuits nano-interconnects, i.e., a low bias condition and an operating frequency up to 1 THz. A generalized non-local dispersive Ohm's law is derived, which can be regarded as the constitutive equation for the material. The behavior of the electrical conductivity is studied with reference to a 2D case (the infinite graphene layer) and a 1D case (the graphene nanoribbons). The modulation effects of the nanoribbons' size and chirality are highlighted, as well as the spatial dispersion introduced in the 2D case by the dyadic nature of the conductivity

A deep learning approach to organic pollutants classification using voltammetry

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about x25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments

Performance Enhancement of Large Crossbar Resistive Memories With Complementary and 1D1R-1R1D RRAM Structures

The paper proposes novel solutions to improve the signal and thermal integrity of crossbar arrays of Resistive Random-Access Memories, that are among the most promising technologies for the 3D monolithic integration. These structures suffer from electrothermal issues, due to the heat generated by the power dissipation during the write process. This paper explores novel solutions based on new architectures and materials, for managing the issues related to the voltage drop along the interconnects and to thermal crosstalk between memory cells. The analyzed memristor is the 1 Diode - 1 Resistor memory. The two architectural solutions are given by a reverse architecture and a complementary resistive switching one. Compared to conventional architectures, both of them are also reducing the number of layers where the bias is applied. The electrothermal performance of these new structures is compared to that of the reference one, for a case-study given by a 4 × 4 × 4 array. To this end, a full-3D numerical Multiphysics model is implemented and successfully compared against other models in literature. The possibility of changing the interconnect materials is also analyzed. The results of this performance analysis clearly show the benefits of moving to these novel architectures, together with the choice of new materials