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Origin of the Shunt Currents and Their Influence on the Ideality Factor (η) of a p-n Junction
Issue:
Volume 10, Issue 5, October 2022
Pages:
180-183
Received:
5 August 2022
Accepted:
25 August 2022
Published:
5 September 2022
DOI:
10.11648/j.jeee.20221005.11
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Abstract: It is our current belief that non-ideal nature of the current – voltage characteristics of a p-n junction is mainly caused by the contribution from generation – recombination currents. It was, however, recently reported in a gate-controlled diode experiment that the ideality factor of a Mercury Cadmium Telluride junction diode exhibits strong dependence on the surface leakage current of the diode, which means that the surface leakage currents are another possible source that may be responsible for the non-ideal I – V characteristics of a junction diode. This work presents a general physical model that provides an insight in to the origin of the shunt currents in a p-n junction as the surface leakage currents are known to be modelled as shunt current. The role of surface leakage currents in influencing the ideality factor of the junction as one of the sources of shunt current therefore constitutes the main theme of the present communication. The investigation of the effect of dislocations, which also contribute to the shunt current, on the ideality factor of the junction is proposed to be a problem for future study. It is concluded that the surface leakage currents owing their origin to recombination currents are responsible for the operation of a shunt resistance in parallel to the junction and consequent degradation in its dynamic impedance. Whereas the previously reported increase in the thermal reverse bias saturation diffusion current of the junction diode is shown to be due to the real time transfer of minority carriers from the one side of the junction to the opposite side. But the degradation in dynamic impedance of the junction is due to apparent reduction in junction barrier hight from exp (qV/kT) to exp (qV/ηkT) by virtue of the operation of the shunt resistance in parallel to the junction impedance.
Abstract: It is our current belief that non-ideal nature of the current – voltage characteristics of a p-n junction is mainly caused by the contribution from generation – recombination currents. It was, however, recently reported in a gate-controlled diode experiment that the ideality factor of a Mercury Cadmium Telluride junction diode exhibits strong depen...
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Lattice Predictors for a Stagger-Period Sequence: Their Theory and Application
Issue:
Volume 10, Issue 5, October 2022
Pages:
184-198
Received:
23 August 2022
Accepted:
13 September 2022
Published:
21 September 2022
DOI:
10.11648/j.jeee.20221005.12
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Abstract: This paper proposes a new structure of lattice predictors, which applies to a stagger-period sequence. To deal with the sequence’s time-varying periods, the stagger-period lattice predictor has a linear time-variant processing structure, whose reflection coefficients and delay units need to match the input sequence periods for optimality. The staggered lattice predictor’s operation is relatively complex, of ∑M forward and backward reflection coefficients, M its order; and a uniform-period lattice predictor consists of M forward and backward coefficients. Based on Burg’s uniform-period lattice algorithm, we propose the staggered Forward and Backward Error Minimum algorithm to determine this predictor’s reflection coefficients and prove its optimality in the sense of minimum mean square error. By means of staggered forward and backward transversal predictors, we also propose the staggered Levinson-Durbin relations and prove it holds in the Appendix; these relations play an important role in researching the staggered lattice predictor. For practical application, we present a corresponding reflection coefficient estimation, the staggered Arithmetic Mean method, which substitutes for the ensemble mean with the limited-sample mean, and minimizes the estimate’s variance in the least square error sense. Through many computer simulations, we investigate convergence performance and learning characteristic of this type of predictor with three observation goals: the reflection coefficient, prediction error, and frequency response; the investigations reveal relationships between the convergence performance, learning characteristic and the balance factor, length of averaging window. In order to apply the staggered lattice predictor to an actual field, we illustrate a moving target indicator for Doppler radar with a stagger-period pulse emission and pulse compression waveform technology. Our simulation tests demonstrate that the staggered block lattice filter with essential artificial intelligence (AI) can efficiently detect weak targets submerged in the stationary and nonstationary clutters. The AI includes five heuristic strategies based on radar professionals’ knowledge to preserve targets and reject false alarms.
Abstract: This paper proposes a new structure of lattice predictors, which applies to a stagger-period sequence. To deal with the sequence’s time-varying periods, the stagger-period lattice predictor has a linear time-variant processing structure, whose reflection coefficients and delay units need to match the input sequence periods for optimality. The stagg...
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Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles
Jorge Vargas,
Antonio Saavedra
Issue:
Volume 10, Issue 5, October 2022
Pages:
199-206
Received:
24 August 2022
Accepted:
9 September 2022
Published:
11 October 2022
DOI:
10.11648/j.jeee.20221005.13
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Abstract: The future of the automotive industry is highly dependent on the integration of electronics into vehicles, particularly as the deployment of advanced electric vehicles (EVs) with varying levels of autonomy have come to fruition. On-board sensors in today's automobiles, such as cameras, radars, lidars, and ultrasonic radars, provide detection and uniformity scenarios in various environments and weather conditions. New technologies have also been deployed, such as 3-D vision and global navigation satellite systems (GNSS). In addition, 5G networks are impacting the development of connected and autonomous vehicles (AVs) making them safer and smarter. The use of on-board sensors in vehicles requires testing, verification, and validation, in order to provide safety, stability, reliability, and precision. Integration of these various systems and networks will aid in the creation of the vehicle-to-everything (V2X) environment. Silicon based integrated circuit (IC) architecture, such as SiGe CMOS and BiCMOS have enabled scaling and cost reduction of advanced sensors. The semiconductor’s value stack of multiple IC architectures, cost advantages, reliability, and sensor fusion can be combined with 5G/mmWave networks for Silicon and Gallium Nitride (GaN) technologies. Advanced materials will also play a pivotal role in driving the further scaling of sensors. These approaches currently play a key role in the process and manufacture of CMOS and MOSFETS. In this paper, an analysis of the current state of advanced sensors is presented, along with semiconductor process advances for AVs. IC innovations such as system integration, sensor local systems, and sensor health are also covered.
Abstract: The future of the automotive industry is highly dependent on the integration of electronics into vehicles, particularly as the deployment of advanced electric vehicles (EVs) with varying levels of autonomy have come to fruition. On-board sensors in today's automobiles, such as cameras, radars, lidars, and ultrasonic radars, provide detection and un...
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Development of Acoustic Optical Fiber Sensor for Arc Discharge in Power Transformer
Issue:
Volume 10, Issue 5, October 2022
Pages:
207-214
Received:
8 October 2022
Accepted:
27 October 2022
Published:
29 October 2022
DOI:
10.11648/j.jeee.20221005.14
Downloads:
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Abstract: Insulation problem is the main cause of power transformer failures and accidents. Arc discharge is an important manifestation and symptom of transformer insulation degradation. Therefore, it is of great significance to realize the online monitoring of arc discharge defects in a timely and effective manner, and to monitor the real-time status of transformers and conduct fault analysis based on them. Previous researches show that when the arc discharge generates ultrasonic signal, it will also be accompanied by a large number of audible signals. Therefore, monitoring the acoustic signal of arc discharge provides a new idea for online monitoring of transformers. In order to fill this research gap, the structure of arc discharge acoustic sensor based on fiber grating in transformer is designed. In view of the problem that the cross sensitivity of stress and temperature affects the measurement accuracy of the sensor, a double fiber grating temperature compensation method is proposed, and a transformer built-in acoustic sensor with large measurement range and excellent anti-interference performance, which has both audible sound and low-frequency ultrasound, is successfully developed. The theoretical measurement range is 1 kHz - 60 kHz. Using the built test platform for sensing the acoustic signal of arc discharge inside the transformer, the acoustic signal sensing ability of the optical fiber acoustic sensor is monitored by comparing the audible acoustic sensor and the ultrasonic sensor. The results show that the acoustic signal band is mainly 2 kHz - 10 kHz and 50 kHz - 60 kHz.
Abstract: Insulation problem is the main cause of power transformer failures and accidents. Arc discharge is an important manifestation and symptom of transformer insulation degradation. Therefore, it is of great significance to realize the online monitoring of arc discharge defects in a timely and effective manner, and to monitor the real-time status of tra...
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