Breakdown probabilities for thin heterostructure avalanche photodiodes

The recurrence theory for the breakdown probability in avalanche photodiodes (APDs) is generalized to heterostructure APDs that may have multiple multiplication layers. The generalization addresses layer-boundary effects such as the initial energy of injected carriers as well as the layer-dependent profile of the dead space in the multiplication region. Reducing the width of the multiplication layer serves to both downshift and sharpen the breakdown probability curve as a function of the applied reverse-bias voltage. In structures where the injected carriers have an initial energy that is comparable to the ionization threshold energy, the transition from linear mode to Geiger-mode is more abrupt than in structures in which such initial energy is negligible. The theory is applied to two recently fabricated Al/sub 0.6/Ga/sub 0.4/As-GaAs heterostructure APDs and to other homostructure thin GaAs APDs and the predictions of the breakdown-voltage thresholds are verified.     

A One‐Kilobit PQR‐CMOS Smart Pixel Array

The photonic quantum ring (PQR) laser is a three dimensional whispering gallery (WG) mode laser and has anomalous quantum wire properties, such as microampere to nanoampere range threshold currents and √T‐dependent thermal red shifts. We observed uniform bottom emissions from a 1‐kb smart pixel chip of a 32×32 InGaAs PQR laser array flip‐chip bonded to a 0.35 µm CMOS‐based PQR laser driver. The PQR‐CMOS smart pixel array, now operating at 30 MHz, will be improved to the GHz frequency range through device and circuit optimization.     

Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

Monolithic SiGe Up‐/Down‐Conversion Mixers with Active Baluns

The purpose of this paper is to describe the implementation of monolithically matching circuits, interface circuits, and RF core circuits to the same substrate. We designed and fabricated on‐chip 1 to 6 GHz up‐conversion and 1 to 8 GHz down‐conversion mixers using a 0.8 µm SiGe hetero‐junction bipolar transistor (HBT) process technology. To fabricate a SiGe HBT, we used a reduced pressure chemical vapor deposition (RPCVD) system to grow a base epitaxial layer, and we adopted local oxidation of silicon (LOCOS) isolation to separate the device terminals. An up‐conversion mixer was implemented on‐chip using an intermediate frequency (IF) matching circuit, local oscillator (LO)/radio frequency (RF) wideband matching circuits, LO/IF input balun circuits, and an RF output balun circuit. The measured results of the fabricated up‐conversion mixer show a positive power conversion gain from 1 to 6 GHz and a bandwidth of about 4.5 GHz. Also, the down‐conversion mixer was implemented on‐chip using LO/RF wideband matching circuits, LO/RF input balun circuits, and an IF output balun circuit. The measured results of the fabricated down‐conversion mixer show a positive power conversion gain from 1 to 8 GHz and a bandwidth of about 4.5 GHz.     

共面转换液晶显示器中获得对比度高于1:600的新技术

薄膜晶体管液晶电视(TFT-LCD TV)因为具有薄、轻、紧凑和可随意放置的特点，已经占据了大部分电视机市场。除了这些物理特性以外，最重要的特性是已具有了良好像质的对比度。为了将对比度提高到1：600以上，对偏振片膜、背光源板、滤色片树脂、电极锥角和摩擦条件等都进行了研究。优化的背光板组合，光滑的电极锥角和摩擦方法的控制是提高对比度的主要控制因素。应用新开发的滤色片树脂，对获得高对比度最为有效。     

Error Concealment Based on Semantic Prioritization with Hardware‐Based Face Tracking

With video compression standards such as MPEG‐4, a transmission error happens in a video‐packet basis, rather than in a macroblock basis. In this context, we propose a semantic error prioritization method that determines the size of a video packet based on the importance of its contents. A video packet length is made to be short for an important area such as a facial area in order to reduce the possibility of error accumulation. To facilitate the semantic error prioritization, an efficient hardware algorithm for face tracking is proposed. The increase of hardware complexity is minimal because a motion estimation engine is efficiently re‐used for face tracking. Experimental results demonstrate that the facial area is well protected with the proposed scheme.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Strength and fracture toughness of nano and micron-silica particles bidispersed epoxy composites: evaluated by fragility parameter

In this study, we discuss the composition effect of 240 nm and 1.56 μm-silica particles on strength and fracture toughness by examining two parameters, fragility and glass transition temperature, that were derived from the thermo-viscoelasticity measurements. Experimental results showed that the composites had a lower fragility with higher strength and fracture toughness as the content of nanoparticles was increased regardless of glass transition temperature. The improvement in mechanical properties from adding nanoparticles was definitely explained by the fragility represented the heterogeneity in polymer matrix, and this was related to the interaction between particles and matrix. The fragility was found to be an effective parameter for evaluating strength and fracture toughness of epoxy composite containing a bidispersion of nano and micron-silica particles.