We aim to provide engineers with an introduction to the nonequilibrium Green's function (NEGF) approach, which is a powerful conceptual tool and a practical analysis method to treat nanoscale electronic devices with quantum mechanical and atomistic effects. We first review the basis for the traditional, semiclassical description of carriers that has served device engineers for more than 50 years. We then describe why this traditional approach loses validity at the nanoscale. Next, we describe semiclassical ballistic transport and the Landauer–Buttiker approach to phase-coherent quantum transport. Realistic devices include interactions that break quantum mechanical phase and also cause energy relaxation. As a result, transport in nanodevices is between diffusive and phase coherent. We introduce the NEGF approach, which can be used to model devices all the way from ballistic to diffusive limits. This is followed by a summary of equations that are used to model a large class of structures such as nanotransistors, carbon nanotubes, and nanowires. Applications of the NEGF method in the ballistic and scattering limits to silicon nanotransistors are discussed.

The operational principles, components, and organization of a Grid-computing infrastructure called In-VIGO (standing for In Virtual Information Grid Organizations)are described. In-VIGO enables computational engineering and science in virtual information Grid organizations. Its distinctive feature is the extensive use of virtualization technologies to provide secure execution environments as needed by tools and users. This paper reviews and motivates the requirements of a cyber infrastructure for computational nanoelectronics. It then explains how such requirements are addressed by the In-VIGO middleware approach, which uses virtualized resources to build computational Grids. The architecture and key design aspects of its first deployed version-In-VIGO 1.0-are presented. It is operational and currently being used to enable the use of computational electronics tools over the Web. Aspects of the design and architecture of the next version of In-VIGO are also presented. It uses Web services standards and components, and lessons learned from In-VIGO 1.0.

We examine current research in nanoelectronics and discuss the role it may play in future electronic systems.

This paper reports on an application of artificial intelligence to achieve demand-based scheduling within the context of a network-computing infrastructure. The described AI system uses tool-specific, run-time input to predict the resource-usage characteristics of runs. Instance-based learning with locally weighted polynomial regression is employed because of the need to simultaneously learn multiple polynomial concepts and the fact that knowledge is acquired incrementally in this domain. An innovative use of a two-level knowledge base allows the system to account for short-term variations in compute-server and network performance and exploit temporal and spatial locality of runs. Instance editing allows the approach to be tolerant to noise and computationally feasible for extended use. The learning system was tested on three tools during normal use of the Purdue University Network Computing Hubs. Results indicate that the described instance-based learning technique using locally weighted regression with a locally linear model works well for this domain.

The numerical solution to Poisson's equation under quasi-equilibrium conditions, using a formulation that includes carrier degeneracy, multiple conduction band minima, nonparabolicity of the Γ valley, and dopant deionization, is discussed. Moment theorems are shown to provide stringent tests for the accuracy of numerically simulated capacitance versus voltage characteristics. The numerical model is also used to quantify errors in a widely used technique for extracting band discontinuities by C-V analysis.