Self-switching diodes as RF rectifiers: evaluation methods and current progress

In the advancement of the Internet of Things (IoT) applications, widespread uses and applications of devices require higher frequency connectivity to be explored and exploited. Furthermore, the size, weight, power and cost demands for the IoT ecosystems also creates a new paradigm for the hardware where improved power efficiency and efficient wireless transmission needed to be investigated and made feasible. As such, functional microwave detectors to detect and rectify the signals transmitted in higher frequency regions are crucial. This paper reviewed the practicability of self switching diodes as Radio Frequency (RF) rectifiers. The existing methods used in the evaluation of the rectification performance and cut-off frequency are reviewed, and current achievements are then concluded. The works reviewed in this paper highlights the functionality of SSD as a RF rectifier with design simplicity, which may offer cheaper alternatives in current high frequency rectifying devices for application in low-power devices

Extraction of Trench Capacitance and Reverse Recovery Time of InGaAs Self-Switching Diode

In this paper, we have presented the transient analysis of an InGaAs based novel nano diode called self-switching device utilizing Silvaco TCAD simulator. The device exhibits current-voltage (I-V) characteristics analogous to a conventional diode without requiring any p-n junction. The cut-in voltage and the output current of the device can be tuned by varying channel width and length, respectively. The charging/discharging time (RC time constants) have been extracted from the I-V characteristics of the device demonstrating almost very small reverse recovery time of the order of 10(-9) s, which significantly affects the device on-off switching. Furthermore, results are validated by implementing the conformal mapping technique to extract device capacitance, which in turn predicts device charging and discharging, and hence, reverse recovery time to enable high frequency operation. Additionally, it is demonstrated that small reverse recovery time enables SSDs to rectify the input signal without requiring additional filter circuitry

Silicon Self-Switching Diode (SSD) as a Full-Wave Bridge Rectifier in 5G Networks Frequencies

The rapid growth of wireless technology has improved the network’s technology from 4G to 5G, with sub-6 GHz being the centre of attention as the primary communication spectrum band. To effectively benefit this exclusive network, the improvement in the mm-wave detection of this range is crucial. In this work, a silicon self-switching device (SSD) based full-wave bridge rectifier was proposed as a candidate for a usable RF-DC converter in this frequency range. SSD has a similar operation to a conventional pn junction diode, but with advantages in fabrication simplicity where it does not require doping and junctions. The optimized structure of the SSD was cascaded and arranged to create a functional full-wave bridge rectifier with a quadratic relationship between the input voltage and outputs current. AC transient analysis and theoretical calculation performed on the full-wave rectifier shows an estimated cut-off frequency at ~12 GHz, with calculated responsivity and noise equivalent power of 1956.72 V/W and 2.3753 pW/Hz1/2, respectively. These results show the capability of silicon SSD to function as a full-wave bridge rectifier and is a potential candidate for RF-DC conversion in the targeted 5G frequency band and can be exploited for future energy harvesting application

Hybrid Statistical and Numerical Analysis in Structural Optimization of Silicon-Based RF Detector in 5G Network

In this study, a hybrid statistical analysis (Taguchi method supported by analysis of variance (ANOVA) and regression analysis) and numerical analysis (utilizing a Silvaco device simulator) was implemented to optimize the structural parameters of silicon-on-insulator (SOI)-based self-switching diodes (SSDs) to achieve a high responsivity value as a radio frequency (RF) detector. Statistical calculation was applied to study the relationship between the control factors and the output performance of an RF detector in terms of the peak curvature coefficient value and its corresponding bias voltage. Subsequently, a series of numerical simulations were performed based on Taguchi’s experimental design. The optimization results indicated an optimized curvature coefficient and voltage peak of 26.4260 V−1 and 0.05 V, respectively. The alternating current transient analysis from 3 to 10 GHz showed the highest mean current at 5 GHz and a cut-off frequency of approximately 6.50 GHz, indicating a prominent ability to function as an RF detector at 5G related frequencies

Hybrid Statistical and Numerical Analysis in Structural Optimization of Silicon-Based RF Detector in 5G Network

In this study, a hybrid statistical analysis (Taguchi method supported by analysis of variance (ANOVA) and regression analysis) and numerical analysis (utilizing a Silvaco device simulator) was implemented to optimize the structural parameters of silicon-on-insulator (SOI)-based self-switching diodes (SSDs) to achieve a high responsivity value as a radio frequency (RF) detector. Statistical calculation was applied to study the relationship between the control factors and the output performance of an RF detector in terms of the peak curvature coefficient value and its corresponding bias voltage. Subsequently, a series of numerical simulations were performed based on Taguchi’s experimental design. The optimization results indicated an optimized curvature coefficient and voltage peak of 26.4260 V−1 and 0.05 V, respectively. The alternating current transient analysis from 3 to 10 GHz showed the highest mean current at 5 GHz and a cut-off frequency of approximately 6.50 GHz, indicating a prominent ability to function as an RF detector at 5G related frequencies