A transparent memory device has been developed based on an indium gallium zinc oxide thin film transistor by incorporating platinum nanoparticles in the gate dielectric stack as the charge storage medium. The transfer characteristics of the device show a large clockwise hysteresis due to electron trapping and are attributed to the platinum nanoparticles. Effect of the gate bias stress (program voltage) magnitude, duration, and polarity on the memory window characteristics has been studied. Charge retention measurements were carried out and a loss of less than 25% of the trapped elec-trons was observed over 104 s indicating promising application as nonvolatile memory.

Molecular-beam epitaxy grown AlxGa1−xN alloys covering the entire range of alloy compositions, 0⩽x⩽1, have been used to determine the alloy band gap dependence on its composition. The Al chemical composition was deduced from secondary ion mass spectroscopy and Rutherford backscattering. The composition was also inferred from x-ray diffraction. The band gap of the alloy was extracted from low temperature optical reflectance measurements which are relatively more accurate than photoluminescence. Fitting of the band gap data resulted in a bowing parameter of b=1.0 eV over the entire composition range. The improved accuracy of the composition and band gap determination and the largest range of the Al composition over which our study has been conducted increase our confidence in this bowing parameter.

No abstract

This paper explores platinum nanoparticle formation during the early stages of growth by atomic layer deposition. Particle size and distribution can be controlled by altering growth parameters. The particles show excellent temperature stability up to 900°C as examined by transmission electron microscopy and in situ heating. Capacitance–voltage and charge retention measurements demonstrate the memory effect in metal-oxide-semiconductor capacitors with embedded nanoparticles. The size, density, charge storage, and temperature stability of the platinum nanoparticles make them attractive for use as charge storage layers for nonvolatile memory devices.

In this paper, an improved modeling approach is described for simulating as-implanted boron impurity profiles for B + and BF2 ~ implants into single*crystal silicon. This method uses the sum of two Pearson distribution functions to account for the nonchanneling and channeling components of the implant distribution. The ratio of the two Pearson functions varies with dose, which accounts for the change in the degree of channeling with dose. This modeling approach has been compared with experimentally measured SIMS profiles for a wide range of energies and doses for shallow B § and BF2 § implants. The excellent agreement indicates that this method offers a large improvement in simulation capability for B § and BF2 § implants. In addition, this method should be applicable to accurately model other impurities which have channeling tendencies.In the development of submicron-integrated circuit technologies, much attention is currently focused on the achievement of reproducible and uniform (over the wafer) shallow, compact impurity profiles for both shallow junctions and the adjustment of various threshold voltages. Also, the entire impurity profile is of interest, in particular for the shallow source-drain junctions in CMOS. This is because of the need to simultaneously have low sheet resist~ ance, very high surface concentration for low contact resistance, a shallow junction, and profile control in order to minimize high electric fields which can cause hot carrier reliability problems. This has been a particularly difficult task for the case of boron due to the strong channeling tendency of this light atom in silicon. Indeed, the degree of channeling has been shown to depend considerably on both the tilt and rotation (or twist) angles during ion implantation, even for those ranges of angles for which channeling is generally considered to be minimal (1-3).Extensive use of process modeling is mandatory for timely, efficient technology development and in order to understand the process control issues in manufacturing. Thus it is necessary to be able to accurately predict impurity profiles beginning with the as-implanted distribution and continuing with the evolution (diffusion) of the profile in subsequent thermal treatments. In addition, the models used to describe the profile evolution in furnace and rapid thermal processing can only be valid over a wide range of conditions if they begin with the correct initial as-implanted impurity distribution. Otherwise, erroneous assumptions must be made in the model in order to relate its predicted diffused profile with the initial implanted profile. This is especially the case when rapid thermal annealing is used.Historically it has been difficult to accurately simulate as-implanted boron distributions for both B+ and BF2 + implants. This is mainly due to the ease with which boron is able to channel in the silicon lattice. Also, at the lower energies of interest for shallow profiles, channeling occurs more easily, further aggravating the simulation difficulty. The original LSS th...