A comparative study of the performance of seven- and 63-chip optical code-division multiple-access encoders and decoders based on superstructured fiber Bragg gratings

We report a range of elementary optical coding and decoding experiments employing superstructured fiber Bragg grating (SSFBG) components: first, we perform a comparative study of the relative merits of bipolar and unipolar coding: decoding schemes and show that the SSFBG approach allows high-quality unipolar and bipolar coding. A performance close to that-theoretically predicted for seven-chip, 160-Gchip/s M-sequence codes is obtained. Second, we report the fabrication and performance of 63-chip, 160-Gchip/s, bipolar Gold sequence grating pairs. These codes are at least eight times longer than those generated by any other scheme based on fiber grating technology so far reported. Last, we describe a range of transmission system experiments for both the seven- and 63-bit bipolar grating pairs. Error-free performance is obtained over transmission distances of ~25 km of standard fiber. In addition, we have demonstrated error-free performance under multiuser operation (two simultaneous users). Our results highlight the precision and flexibility of our particular grating writing process and show that SSFBG technology represents a promising technology not just for optical code division multiple access (OCDMA) but also for an extended range of other pulse-shaping optical processing applications

Integrated Circuit Packaging Recognition with Tilt Auto Adjustment using Deep Learning Approach

A deep-learning-based approach for recognizing integrated circuit (IC) packaging type is presented in this paper. The objective of this work is to design a deep-learning method that can recognize multiple types of packaging per detection, performing counting operations, and calculating the centre location of an IC with its tilting angle. The transfer learning from model You-Only-Look-Once (YOLO) v5 was chosen because it has been trained with the coco dataset and has a more reliable feature extraction system than the other models. In order to extract data from images, OpenCV was used, which allows the deep learning model to perform more efficient analysis of the input data. Apart from that, the principal component analysis (PCA) was used to estimate the angle of the IC in order to determine the rotation of each IC for the purpose of tilting adjustment. The developed model has an average confidence score of 85% and is capable of operating in a variety of conditions, as demonstrated by ANOVA analysis

Design and Analysis of 15 nm MOSFETs

We present the design and analysis of 15 nm NMOS transistors, fabricated on three different substrate materials -- namely silicon, indium nitride and indium arsenide. Close inspection on the I-V characteristic curves reveals that the saturation voltage and current of the indium arsenide transistors are significantly higher than the other two counterparts. We attribute this result to the high mobility of carriers in indium arsenide substrate. It is also observed that the breakdown voltages of the indium arsenide transistors are also one of the highest. The breakdown behaviour shows that transistors fabricated on indium arsenide substrate renders reasonably high robustness. Due to high channel length modulation effect, it could also be seen that current variation between saturation and breakdown currents is the highest in the conventional silicon transistors. Our analysis suggests that indium arsenide could be an alternative substrate material in the design and fabrication of nano-scale MOSFETs. For devices which may require high power consumption (and therefore high current and voltage), indium arsenide can also be considered as an appropriate substrate material

Multi-wavelength (40 WDM x 10 Gbit/s) optical packet router based on superstructure fibre Bragg gratings

We demonstrate a multi-wavelength (40 WDM x 10 Gbit/s) optical packet router capable of processing 4 Gigapacket/s based on all-optical label generation and recognition using 16-bit, 20 Gbit/s four-level phase coding superstructure fibre Bragg gratings. Error free operation is obtained for the switched packets when all 40 channels are transmitting simultaneously

Modeling Electrostatic Separation Process Using Artificial Neural Network (ANN)

AbstractIn this paper, the characteristics of an electrostatic separator were modeled using artificial neural network (ANN). The model was constructed by considering the misclassified middling product during separation, where system parameters (voltage level, rotation speed, electrode position, etc) were varied. The ANN architecture was optimized through the variation in the neuron number, percentage of testing data and percentage of validation data. Performance of the network was assessed by the error indicators, namely mean square error (MSE) and coefficient of determination (R-square). It is found that, lesser number of neurons and lower percentage of both training and validation dataset contributes to better network performance. Additionally, network architecture thus derived was selected for a detailed study on the various combinations performance corresponding to the input and output variables. The results consequently suggest a simplified network structure with reduced number of input variables for modeling of this nonlinear process