Quantum Computing is a technology, which promises to overcome the drawbacks of conventional CMOS technology for high density and high performance applications. Its potential to revolutionize today's computing world is attracting more and more researchers towards this field. However, due to the involvement of quantum properties, many beginners find it difficult to follow the field. Therefore, in this research note an effort has been made to introduce the various aspects of quantum computing to researchers, quantum engineers and scientists. The historical background and basic concepts necessary to understand quantum computation and information processing have been introduced in a lucid manner. Various physical implementations and potential application areas of quantum computation have also been discussed in this paper. Recent developments in each realization, in the context of the DiVincenzo criteria, including ion traps based quantum computing, superconducting quantum computing, nuclear magnetic resonance (NMR) quantum computing, spintronics and semiconductor based quantum computing have been discussed.

The rapid advancement of new technologies has resulted in a surge of data, while conventional computers are nearing their computational limits.

In this paper, we propose and simulate a novel Schottky barrier MOSFET (SB-MOSFET) with improved performance in comparison to the conventional SB-MOSFET. The proposed device employs charge plasma/electrostatic doping-based dopant segregation layers (DSLs) by using metal extensions (Hafnium) at the source and drain top and bottom edges. The metal hafnium induces charge plasma and hence realizes DSLs adjacent to source and drain regions. These charge plasma DSLs reduce the tunneling width and hence enhances the performance of the proposed device. The proposed electrostatically doped DSL-based SB-MOSFET has all the advantages of the conventional SB-MOSFET and it outperforms the state of the art doped DSL-based MOSFET. A two-dimensional calibrated simulation study has shown that the ON current (I_ON), I_ON/I_OFF ratio, subthreshold-swing and cutoff frequency (f_T) of the proposed device have been increased by ~420 times, ~1000 times, 14.5%, and 290 times in comparison to conventional SB-MOSFET, respectively. The transient analyses have shown that a reduction by 16 times in ON delay and 6 times in OFF delay in the proposed device-based inverter in comparison to the conventional SB-MOSFET-based inverter.

In this work, a novel structure of Dielectrically Modulated Organic Thin Film Transistor (OTFT) for biosensing applications is presented. The proposed device uses lateral metal contacts and an implanted metal with work function of 5.7 eV inside the organic material; as such it is being named as Metal Controlled Organic Thin Film Transistor (MC-OTFT). The performed 2D simulation study has revealed that the proposed MC-OTFT shows significantly higher sensitivity for different dielectric constants and their related charge densities. Besides, the calibrated simulation study has shown that the change in ON current (ION sensing factor calculated for VDS = -1.5V, VGS = -3 V) in MC-OTFT is significantly higher (max. of 44.40 times for neutral, 44.55 times for charged biomolecule at ρ = -1x1012 cm-2). In addition, a considerable enhancement in sensitivity at low temperatures has been observed. The proposed device therefore offers a huge potential for biosensing applications.

In this work, a single transistor based on germanium (Ge) is used to construct a leaky integrate-and-fire (LIF) neuron with significant improvement in energy efficiency, area efficiency, and reduction in cost. Using 2-D calibrated simulation, we validated that Ge-MOSFET LIF neuron is able to imitate the neuron behavior accurately. The Ge-MOSFET shows low breakdown voltage, high impact ionization coefficient, and sharp breakdown. All these factors are responsible for achieving low energy per spike and higher spiking current. The proposed Ge-MOSFET-based spiking LIF neuron needs only 8 pJ/spike of energy as compared to recently reported silicon-based silicon-on-insulator (SOI) MOSFET, which needs 45 pJ/spike of energy. The use of gate voltage makes Ge-MOSFET LIF neuron firing controllable, which can improve the energy efficiency of the spiking neural network (SNN) by inducing sparse action.