High Output Saturation and High-Linearity Uni-Traveling-Carrier Waveguide Photodiodes

Waveguide uni-traveling-carrier photodiodes (UTC-PDs) have been fabricated and tested. Output saturation currents greater than 40 mA at 1 GHz are demonstrated for a 10 mumtimes150mum photodiode (PD). The third-order intermodulation distortion is also measured and exhibits a third-order output intercept point of 43 dBm at 20 mA and 34 dBm at 40 mA for this same PD. UTC-PDs with geometries of 5 mumtimes100 mum and 10 mumtimes100 mum are also compared and it is shown that a wider waveguide PD has improved saturation characteristics due to the lower optical power density which reduces the saturation at the front end of the device     

Travelling-wave photodetectors with 172-GHz bandwidth and 76-GHzbandwidth-efficiency product

Results of the first fabrication and measurement of travelling-wave photodetectors are reported. The devices have bandwidths as high as 172 GHz, the highest reported for a p-i-n photodetector, and bandwidth-efficiency products as large as 76 GHz, the largest reported for any photodetector without gain. Comparisons with vertically illuminated and waveguide photodetectors fabricated on the same wafer establish the superior performance of travelling-wave photodetectors. Microwave loss on the travelling-wave photodetector structure is identified as a bandwidth limitation     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

Numerical analysis on compliance and electrical behavior of multi-copper-column flip-chip interconnects for wafer-level packaging

This paper presents modeling and simulation results of a modified copper-column-based flip-chip interconnect with ultrafine pitch for wafer-level packaging, and the process and prototyping procedure are described as well. This interconnect consists of multiple copper columns which are electrically in parallel and supporting a solder bump. A simple analytical model has been developed for correlation between the interconnect geometry and the thermal fatigue life. In comparison to the conventional single-copper-column (SCC) interconnects, numerical analysis reveals that the multi-copper-column (MCC) interconnect features enhanced compliances and, hence, higher thermomechanical reliability, while the associated electrical parasitics (R, L, and C) at dc and moderate frequencies are still kept low. Parametric studies reveal the effects of geometric parameters of MCC interconnects on both compliances and electrical parasitics, which in turn facilitate design optimization for best performance. By using coplanar waveguides (CPWs) as feed lines on both chip and package substrate, a high-frequency (up to 40 GHz) S-parameter analysis is conducted to investigate the transmission characteristics of the MCC interconnects within various scenarios which combines various interconnect pitches and common chip and package substrates. An equivalent lumped circuit model is proposed and the circuit parameters (R, L, C, and G) are obtained throughout a broad frequency range. Good agreement is achieved for the transmission characteristics between the equivalent lumped circuit model and direct simulation results.     

A Spatiotemporal Framework for Estimating Trial-to-Trial Amplitude Variation in Event-Related MEG/EEG

A spatiotemporal framework for estimating trial-to-trial variability in evoked response (ER) data is presented. Spatial and temporal bases capture the aspects of the response that are consistent across trials, while the basis expansion coefficients represent the variable components of the response. We focus on the simplest case of constant spatiotemporal response shape and varying amplitude across trials. Two different constraints on the amplitude evolution are employed to effectively integrate the individual responses and improve robustness at low SNR. The linear dynamical system response constraint estimates the current trial amplitude as an unknown constant scaling of the estimate in the previous trial plus zero-mean Gaussian noise with unknown variance. The independent response constraint estimates response amplitudes across trials as independent Gaussian random variables having unknown mean and variance. We develop a generalized expectation-maximization algorithm to obtain the maximum-likelihood (ML) estimates of the signal waveform, noise covariance matrix, and unknown constraint parameters. ML source localization is achieved by scanning the likelihood over different sets of spatial bases. We demonstrate the variability estimation and source localization effectiveness of the proposed algorithms using both real and simulated ER data.     

Novel Optical Phase Demodulator Based on a Sampling Phase-Locked Loop

A novel phase-locked coherent demodulator, based on a sampling phase-locked loop, is presented and investigated theoretically. The demodulator is capable of operating at high frequencies, by using optical sampling to downconvert the high-frequency input radio-frequency signal to the frequency range of the baseband loop. We develop a detailed theoretical model of the (sampling) phase-locked coherent demodulator and perform detailed numerical simulations. The simulation results show that the operation of the sampling demodulator resembles the operation of the baseband demodulator for very short optical pulses (<2 ps). Furthermore, we investigate how the signal-to-noise ratio of the demodulator is affected by timing and amplitude jitter of the pulsed optical source     

Recovery of second-order optical nonlinearity in annealedproton-exchanged LiNbOx

The second-order nonlinear coefficients measured in proton-exchanged LiNbO₃ as a function of annealing time are discussed. Measurements of reflected second-harmonic power indicate that the second-order nonlinear coefficient d₃₃ is reduced to 60% of the bulk value as a result of proton exchange in pure benzoic acid. It is also shown that annealing restores the d-coefficients to almost the original value of the virgin crystal. For example, recovery to ~90% of the bulk value was obtained for a sample with a 0.3-μm-bulk proton-exchanged layer, annealed for 10 h at 350°C     

Improved Hydrogen Capping Effect in n-Type Crystalline Silicon Solar Cells by SiN(Si-Rich)/SiN(N-Rich) Stacked Passivation

The effect of hydrogen capping of SiN(Si-rich)/SiN(N-rich) stacks for n-type c-Si solar cells was investigated. Use of a passivation layer consisting of Si-rich SiN with a refractive index (n) of 2.7 and N-rich SiN with a refractive index of 2.1 improved the thermal stability. A single SiN passivation layer with a refractive index of 2.05 resulted in an initial lifetime of 200 μs whereas the layer with a refractive index of 2.7 resulted in a high initial lifetime of 2 ms, but the layer degraded rapidly after firing. A stacked passivation layer with refractive indices of 2.1 and 2.7 had a stable lifetime of 1.5 ms with an implied open-circuit voltage (iV _oc) of 720 mV after firing. The thermally stable passivation mechanism with changing amounts of Si–N and Si–H bonding was analyzed by Fourier-transform infrared (FTIR) spectroscopy. Incorporation of the SiN _x stack layer (2.7 + 2.1) into the passivated rear of n-type Cz silicon screen-printed solar cells resulted in energy conversion efficiency of 19.69%. Improved internal quantum efficiency in the long-wavelength range above 900 nm, with V _oc of 630 mV, is mainly because of superior passivation of the rear surface compared with conventional solar cells.