III-nitride light-emitting devices have been subjects of intense research for the last several decades owing to the versatility of their applications for fundamental research, as well as their widespread commercial utilization. Nitride light-emitters in the form of light-emitting diodes (LEDs) and lasers have made remarkable progress in recent years, especially in the form of blue LEDs and lasers. However, to further extend the scope of these devices, both below and above the blue emission region of the electromagnetic spectrum, and also to expand their range of practical applications, a number of issues and challenges related to the growth of materials, device design, and fabrication need to be overcome. This review provides a detailed overview of nitride-based LEDs and lasers, starting from their early days of development to the present state-of-the-art light-emitting devices. Besides delineating the scientific and engineering milestones achieved in the path towards the development of the highly matured blue LEDs and lasers, this review provides a sketch of the prevailing challenges associated with the development of long-wavelength, as well as ultraviolet nitride LEDs and lasers. In addition to these, recent progress and future challenges related to the development of next-generation nitride emitters, which include exciton-polariton lasers, spin-LEDs and lasers, and nanostructured emitters based on nanowires and quantum dots, have also been elucidated in this review. The review concludes by touching on the more recent topic of hexagonal boron nitride-based light-emitting devices, which have already shown significant promise as deep ultraviolet and single-photon emitters.

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here, we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insight to circumvent these challenges for the future progression of research.

A scalable cryogenic memory system is one of the prime requirements for the implementation of practical quantum computers, large-scale single flux quantum circuits, and space electronics. Here, we leverage the memristive behavior of a conductance-asymmetric superconducting quantum interference device (CA SQUID) to design an ultra-fast and low-power memory system. We develop a physics-based circuit-compatible model for CA-SQUID-based superconducting memristors (ScMs). Using this compact model, we design and test an ScM-based nonvolatile cryogenic memory system and explore the design space. Via analyzing the sensitivity and tunability of the device hysteresis up to the array level, we provide a comprehensive guideline for its experimental realization. The ScM-based memory system has the potential to solve the scalability issue of the state-of-the-art superconducting data storage systems and may trigger rapid advancement in quantum computing, space electronics, and cryogenic neuromorphic systems.

We present a Verilog-A based compact model for the superconductor-insulator-superconductor (SIS) Josephson junction. The model can generate both hysteretic and non-hysteretic current-voltage ( I-V ) response for the SIS junctions utilizing the Stewart-McCumber parameter. We calibrate our model with different SIS samples and demonstrate accurate matching between the simulated and experimental results. We implement temperature effect on the energy gap and the critical current of the superconductor to explore the dynamic trends in device characteristics. We calculate the junction inductance and energy as functions of junction current and temperature. We simulate the read/write operations of an SIS junction based cryogenic memory cell to illustrate the usability of our model.

We present a unique compact model for oxide memristors based upon the concentration of oxygen vacancies as state variables. In this model, the increase (decrease) in oxygen vacancy concentration is similar in effect to the reduction (expansion) of the tunnel gap used as a state variable in existing compact models, providing a mechanism for the electronic current to increase (decrease) based upon the polarity of the applied voltage. Rate equations defining the dynamics of state variables are obtained from simplifications of a recent paper in which electronic processes (i.e., electron capture/emission) were combined with atomic processes (i.e., Frenkel-pair generation/recombination, diffusion) stemming from the thermochemical model of dielectric breakdown. Central to the proposed model is the effect of the electron occupancy of oxygen vacancy traps on resistive switching dynamics. The electronic current is calculated considering Ohmic, band-to-band, and bound-to-band contributions. The model includes uniform self-heating with Joule heating and conductive loss terms. The model is calibrated using experimental current–voltage characteristics for $\mathrm{Hf}{\mathrm{O}}_2$ memristors with different electrode materials. Though a general model is presented, a delta-shaped density of states profile for oxygen vacancies is found capable of accurately representing experimental data while providing a minimal description of bound-to-band transitions. The model is implemented in Verilog-A and tested using read/write operations in a $4\times 4$ 1T1R nonvolatile memory array to evaluate its ability to perform circuit simulations of practical interest. A particular benefit is that the model does not make strong assumptions regarding filament geometry of which scant experimental-evidence exists to support.