Excess noise peaks in porous silicon-based diode structures

Results of an experimental observation of the voltage oscillations associated with a discrete tunneling of holes in porous silicon at room temperature are presented. The noise characteristics of diode structures with a porous silicon interlayer formed on heavily boron-doped silicon single crystals are studied. Peaks of excessive noise are observed at frequencies of ～1 MHz, at which single-electron oscillations should be expected. The peak noise power is found to increase with current according to the ～2.5 power law and, at a current density of 0.15 A/cm², to exceed the noise power of the receiver by three to four orders of magnitude. The complex shape of the noise spectrum and its extension to the higher frequency region with increasing current are explained by the three-dimensionality of the system of nanometer-sized silicon grains embedded in insulating silicon dioxide of porous silicon.     

Phase modulation of the signal beam in three-wave forward mixing in photorefractive bismuth silicon oxide

The interaction of three forward beams in a BSO crystal is investigated under conditions when the two pump beams are anti-symmetrically detuned and the signal beam is phase modulated. For sinusoidal phase modulation the signal gain is shown to be dependent on the instantaneous frequency detuning. Single and double maxima in gain are obtained depending on the voltage amplitude applied to the piezoelectric mirror. For triangular phase modulation a slight asymmetry is found in the gain versus detuning curve.     

Characterization of spherical particles using high-order neural networks and scanning flow cytometry

We retrieve the radius R, real n and imaginary k parts of the refractive index of homogeneous spherical particles using angular distribution of the light-scattering intensity. To solve the inverse light-scattering problem we use a high-order neural-network technique. The effect of network parameters on optimization is examined. The technique is evaluated for noise-corrupted input data at 0.6 μm<R<10.6 μm, 1.02<n<1.38, and 0<k<0.03. The errors of retrieval for nonabsorbing particles do not exceed 0.05 μm for radius and 0.015 for refractive index. The experimental verification is fulfilled by experimental data retrieved by means of a scanning flow cytometer. The light-scattering profiles of polystyrene beads and spherized red blood cells are processed with the high-order neural networks and a non-linear regression at Mie theory. The parameters retrieved by the high-order neural networks correlate well with the parameters retrieved by the least-square method.     

Pushing Squares Around

We study dynamic self-reconfiguration of modular metamorphic systems. We guarantee the feasibility of motion planning in a rectangular model consisting of square modules that are allowed to slide along or rotate about one another. That is, we show that any two connected configurations of the same number of modules can be transformed into each other by a sequence of moves so that all intermediate configurations are connected. This settles a conjecture formulated in [6].