Insights into the Mechanical and Electrical Properties of a Metal-Phosphorene Interface: An Ab Initio Study with a Wide Range of Metals.

Finding a metal contact with higher interface adhesion and lower contact resistivity is a major challenge in realizing 2D material-based field-effect transistors. The commonly used metals in the semiconductor industry have different interface chemistry with phosphorene. Although phosphorene FETs have been fabricated with gold, titanium, and palladium contacts, there are other metals with a better interface. In this work, using DFT, a systematic ab initio study of metal-phosphorene interfaces is carried out for a set of 18 potentially suitable metals with different resistivity, electronegativity, and work-function. The interface between these metals and phosphorene is studied to identify factors responsible for mechanical and electrical behavior of the metal contacts. The work of separation is calculated to measure the adhesion strength of the metal contacts, while the density of states, Schottky barrier height, tunnel barrier height, and the mid-interface charge density calculations are performed to analyze the electrical behavior. Both mechanical and electrical performance of the metal contacts are linked to the interface chemistry. Many important observations which deviate from the general trend are reported and explained.

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.

Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~1011 neuron based) large neural networks.