Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Modeling of Thermally Aware Carbon Nanotube and Graphene Based Post CMOS VLSI Interconnect

This work studies various emerging reduced dimensional materials for very large-scale integration (VLSI) interconnects. The prime motivation of this work is to find an alternative to the existing Cu-based interconnect for post-CMOS technology nodes with an emphasis on thermal stability. Starting from the material modeling, this work includes material characterization, exploration of electronic properties, vibrational properties and to analyze performance as a VLSI interconnect. Using state of the art density functional theories (DFT) one-dimensional and two-dimensional materials were designed for exploring their electronic structures, transport properties and their circuit behaviors. Primarily carbon nanotube (CNT), graphene and graphene/copper based interconnects were studied in this work. Being reduced dimensional materials the charge carriers in CNT(1-D) and in graphene (2-D) are quantum mechanically confined as a result of this free electron approximation fails to explain their electronic properties. For same reason Drude theory of metals fails to explain electronic transport phenomena. In this work Landauer transport theories using non-equilibrium Green function (NEGF) formalism was used for carrier transport calculation. For phonon transport studies, phenomenological Fourier’s heat diffusion equation was used for longer interconnects. Semi-classical BTE and Landauer transport for phonons were used in cases of ballistic phonon transport regime. After obtaining self-consistent electronic and thermal transport coefficients, an equivalent circuit model is proposed to analyze interconnects’ electrical performances. For material studies, CNTs of different variants were analyzed and compared with existing copper based interconnects and were found to be auspicious contenders with integrational challenges. Although, Cu based interconnect is still outperforming other emerging materials in terms of the energy-delay product (1.72 fJ-ps), considering the electromigration resistance graphene Cu hybrid interconnect proposed in this dissertation performs better. Ten times more electromigration resistance is achievable with the cost of only 30% increase in energy-delay product. This unique property of this proposed interconnect also outperforms other studied alternative materials such as multiwalled CNT, single walled CNT and their bundles

A survey of carbon nanotube interconnects for energy efficient integrated circuits

This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design

Carbon Nanotube Interconnect Modeling for Very Large Scale Integrated Circuits

In this research, we have studied and analyzed the physical and electrical properties of carbon nanotubes. Based on the reported models for current transport behavior in non-ballistic CNT-FETs, we have built a dynamic model for non-ballistic CNT-FETs. We have also extended the surface potential model of a non-ballistic CNT-FET to a ballistic CNT-FET and developed a current transport model for ballistic CNT-FETs. We have studied the current transport in metallic carbon nanotubes. By considering the electron-electron interactions, we have modified two-dimensional fluid model for electron transport to build a semi-classical one-dimensional fluid model to describe the electron transport in carbon nanotubes, which is regarded as one-dimensional system. Besides its accuracy compared with two-dimensional fluid model and Lüttinger liquid theory, one-dimensional fluid model is simple in mathematical modeling and easier to extend for electronic transport modeling of multi-walled carbon nanotubes and single-walled carbon nanotube bundles as interconnections. Based on our reported one-dimensional fluid model, we have calculated the parameters of the transmission line model for the interconnection wires made of single-walled carbon nanotube, multi-walled carbon nanotube and single-walled carbon nanotube bundle. The parameters calculated from these models show close agreements with experiments and other proposed models. We have also implemented these models to study carbon nanotube for on-chip wire inductors and it application in design of LC voltage-controlled oscillators. By using these CNT-FET models and CNT interconnects models, we have studied the behavior of CNT based integrated circuits, such as the inverter, ring oscillator, energy recovery logic; and faults in CNT based circuits