The use of neural modeling tools is becoming increasingly common in the exploration of human brain behavior, enabling effective simulations through event-driven methodologies. As a result, years of study and advancements in the field of neurotechnology have led to the creation of several artificial neural network approaches that mimic biological neural networks. The event-driven approach provides an effective method for mimicking large-scale spiking neural networks (SNNs), by taking advantage of the brain’s sparse processing. This paper investigates SNN employing a leaky integrate-and-fire neuron model with latency estimation through FNS. A three-layer feedforward network (FFN) is constructed, incorporating design parameters from Config Wizard. Notably, our study sheds light on the impact of synchrony within a simple FFN. Through the incorporation of biologically plausible delay effects, our model offers novel insights that complement the existing literature. Neural activity is organized in CSV format files, facilitating the reconstruction of electrophysiological-like signals. FNS enables a comprehensive exploration of interactions within and between populations of spiking neurons. In the near future, we intend to use these findings in situations where this particular class of neural networks and digital signal processing (DSP) applications can be combined to create potent nonlinear DSP techniques.

This review article discusses the development of gallium arsenide (GaAs)-based resonant tunneling diodes (RTD) since the 1970s. To the best of my knowledge, this article is the first review of GaAs RTD technology which covers different epitaxial-structure design, fabrication techniques, and characterizations for various application areas. It is expected that the details presented here will help the readers to gain a perspective on the previous accomplishments, as well as have an outlook on the current trends and future developments in GaAs RTD research.

This article reports on the development of a simple two-step lithography process for double barrier quantum well (DBQW) InGaAs/AlAs resonant tunneling diode (RTD) on a semi-insulating indium phosphide (InP) substrate using an air-bridge technology. This approach minimizes processing steps, and therefore the processing time as well as the required resources. It is particularly suited for material qualification of new epitaxial layer designs. A DC performance comparison between the proposed process and the conventional process shows approximately the same results. We expect that this novel technique will aid in the recent and continuing rapid advances in RTD technology.