AlN/GaN Metal Insulator Semiconductor Field Effect Transistors (MISFETs) were designed, simulated and fabricated. DC, S-parameter and power measurements were also performed. Drift-diffusion simulations using DESSIS compared AlN/GaN MISFETs and Al32Ga68N/GaN Heterostructure FETs (HFETs) with the same geometries. The simulation results show the advantages of AlN/GaN MISFETs in terms of higher saturation current, lower gate leakage and higher transconductance than AlGaN/GaN HFETs. First results from fabricated AlN/GaN devices with 1 µm gate length and 200 µm gate width showed a maximum drain current density of 380 mA/mm and a peak extrinsic transconductance of 85 mS/mm. S-parameter measurements showed that current-gain cutoff frequency (fT) and maximum oscillation frequency (fmax) were 5.85 GHz and 10.57 GHz, respectively. Power characteristics were measured at 2 GHz and showed output power density of 850 mW/mm with 23.8% PAE at VDS = 15 V. To the authors knowledge this is the first report of a systematic study of AlN/GaN MISFETs addressing their physical modeling and experimental high-frequency characteristics including the power performance.

There is increasing evidence from in vivo recordings in monkeys trained to respond to stimuli by making left- or rightward eye movements, that firing rates in certain groups of neurons in oculo-motor areas mimic drift-diffusion processes, rising to a (fixed) threshold prior to movement initiation. This supplements earlier observations of psychologists, that human reaction-time and error-rate data can be fitted by random walk and diffusion models, and has renewed interest in optimal decision-making ideas from information theory and statistical decision theory as a clue to neural mechanisms. We review results from decision theory and stochastic ordinary differential equations, and show how they may be extended and applied to derive explicit parameter dependencies in optimal performance that may be tested on human and animal subjects. We then briefly describe a biophysically-based model of a pool of neurons in locus coeruleus, a brainstem nucleus implicated in widespread norepinephrine release. This neurotransmitter can effect transient gain changes in cortical circuits of the type that the abstract drift-diffusion analysis requires. We also describe how optimal gain schedules can be computed in the presence of time-varying noisy signals. We argue that a rational account of how neural spikes give rise to simple behaviors is beginning to emerge.

In this work it is shown for the first time how to calculate in advance by momentum-based noise simulation for stationary Monte Carlo (MC) device simulations the CPU time, which is necessary to achieve a predefined error level. In addition, analytical expressions for the simulation-time factor of terminal current estimation are given. Without further improvements of the MC algorithm MC simulations of small terminal currents are found to be often prohibitively CPU intensive.

The full-band Monte Carlo technique is currently the most accurate device simulation method, but its usefulness is limited because it is very CPU intensive. This work describes efficient algorithms in detail, which raise the efficiency of the full-band Monte Carlo method to a level where it becomes applicable in the device design process beyond exemplary simulations. The k-space is discretized with a nonuniform tetrahedral grid, which minimizes the discretization error of the linear energy interpolation and memory requirements. A consistent discretization of the inverse mass tensor is utilized to formulate efficient transport parameter estimators. Particle scattering is modeled in such a way that a very fast rejection technique can be used for the generation of the final state eliminating the main cause of the inefficiency of full-band Monte Carlo simulations. The developed full-band Monte Carlo simulator is highly efficient. For example, in conjunction with the nonself-consistent simulation technique CPU times of a few CPU minutes per bias point are achieved for substrate current calculations. Self-consistent calculations of the drain current of a 60nm-NMOSFET take about a few CPU hours demonstrating the feasibility of full-band Monte Carlo simulations.

We propose a nonlocal impact ionization model applicable for the drain region where electric field increases exponentially. It is expressed as a function of an electric field and a characteristic length which is determined by a thickness of gate oxide and a source/drain junction depth. An analytical substrate current model for n-MOSFET is also derived from the new nonlocal impact ionization model. The model well explains the reason why the theoretical characteristic length differs from empirical expressions used in a pseudo two-dimensional model for MOSFET's. The nonlocal impact ionization model implemented in a device simulator demonstrates that the new model can predict substrate current correctly in the framework of drift-diffusion model.

The performance of a drift-diffusion device simulator using massively parallel processors is improved by modifying the preconditioner for the iterative solver and by improving the initial guess for the Newton loop. A grid-to-processor mapping scheme is presented to implement the partitioned natural ordering preconditioner on the CM-5. A new preconditioner called the block partitioned natural ordering, which may include fill-ins, improves performance in terms of CPU time and convergence behavior on the CM-5. A multigrid discretization to implement a block Newton initial guess routine is observed to decrease the CPU time by a factor of two. Extensions of the initial guess routine show further reduction in the final fine grid linear iterations.