Multi-Molecule Field-Coupled Nanocomputing for the Implementation of a Neuron

In recent years, several alternatives have been proposed to face CMOS scaling problems. Among these, molecular Field-Coupled Nanocomputing is a paradigm that encodes information in the spatial charges distribution and promises to consume a minimal amount of power. In this technology, circuits have always been designed using the same molecule type, and logic functions are obtained through specific layouts. This work demonstrates that multi-molecule circuits, which use different kinds of molecules in the same layout, enhance the circuit features and set up a new way to conceive molecular Field-Coupled Nanocomputing. In particular, by inserting different molecules with specific characteristics into appropriate layout positions, it is possible to obtain an artificial neuron behavior using the Majority Voter layout

SCERPA Simulation of Clocked Molecular Field-Coupling Nanocomputing

Among all the possible technologies proposed for post-CMOS computing, molecular field-coupled nanocomputing (FCN) is one of the most promising technologies. The information propagation relies on electrostatic interactions among single molecules, overcoming the need for electron transport, significantly reducing energy dissipation. The expected working frequency is very high, and high throughput may be achieved by introducing an efficient pipeline of information propagation. The pipeline could be realized by adding an external clock signal that controls the propagation of data and makes the transmission adiabatic. In this article, we extend the Self-Consistent Electrostatic Potential Algorithm (SCERPA), previously introduced to analyze molecular circuits with a uniform clock field, to clocked molecular devices. The single-molecule is analyzed by ab initio calculations and modeled as an electronic device. Several clocked devices have been partitioned into clock zones and analyzed: the binary wire, the bus, the inverter, and the majority voter. The proposed modification of SCERPA enables linking the functional behavior of the clocked devices to molecular physics, becoming a possible tool for the eventual physical design verification of emerging FCN devices. The algorithm provides some first quantitative results that highlight the clocked propagation characteristics and provide significant feedback for the future implementation of molecular FCN circuits

Artificial intelligence in nanotechnology

This is the author’s version of a work that was accepted for publication in Nanotechnology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Nanotechnology 24.45 (2013): 452002During the last decade there has been an increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial intelligence based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines

A Model for the Evaluation of Monostable Molecule Signal Energy in Molecular Field-Coupled Nanocomputing

Molecular Field-Coupled Nanocomputing (FCN) is a computational paradigm promising high-frequency information elaboration at ambient temperature. This work proposes a model to evaluate the signal energy involved in propagating and elaborating the information. It splits the evaluation into several energy contributions calculated with closed-form expressions without computationally expensive calculation. The essential features of the 1,4-diallylbutane cation are evaluated with Density Functional Theory (DFT) and used in the model to evaluate circuit energy. This model enables understanding the information propagation mechanism in the FCN paradigm based on monostable molecules. We use the model to verify the bistable factor theory, describing the information propagation in molecular FCN based on monostable molecules, analyzed so far only from an electrostatic standpoint. Finally, the model is integrated into the SCERPA tool and used to quantify the information encoding stability and possible memory effects. The obtained results are consistent with state-of-the-art considerations and comparable with DFT calculation

Investigation of Molecular FCN for Beyond-CMOS: Technology, design, and modeling for nanocomputing

L'abstract è presente nell'allegato / the abstract is in the attachmen

SCERPA: a Self-Consistent Algorithm for the Evaluation of the Information Propagation in Molecular Field-Coupled Nanocomputing

Among the emerging technologies that are intended to outperform the current CMOS technology, the field-coupled nanocomputing (FCN) paradigm is one of the most promising. The molecular quantum-dot cellular automata (MQCA) has been proposed as possible FCN implementation for the expected very high device density and possible room temperature operations. The digital computation is performed via electrostatic interactions among nearby molecular cells, without the need for charge transport, extremely reducing the power dissipation. Due to the lack of mature analysis and design methods, especially from an electronics standpoint, few attempts have been made to study the behavior of logic circuits based on real molecules, and this reduces the design capability. In this article, we propose a novel algorithm, named self-consistent electrostatic potential algorithm (SCERPA), dedicated to the analysis of molecular FCN circuits. The algorithm evaluates the interaction among all molecules in the system using an iterative procedure. It exploits two optimizations modes named Interaction Radius and Active Region which reduce the computational cost of the evaluation, enabling SCERPA to support the simulation of complex molecular FCN circuits and to characterize consequentially the technology potentials. The proposed algorithm fulfills the need for modeling the molecular structures as electronic devices and provides important quantitative results to analyze the information propagation, motivating and supporting further research regarding molecular FCN circuits and eventual prototype fabrication