Computing technology and power continue to advance rapidly. Component sizes for computing continue to diminish over time. Unfortunately, bulk materials continue to impose constraints that limit component sizes. Nanotechnology offers one potential solution, using bottom-up techniques that produce nanomaterials which are much smaller than what can be manufactured through other methods.
Two-dimensional materials hold great fascination due to their small sizes, customizability and electronic properties which range from highly conductor to semiconducting properties depending on the 2D material chosen. There is an array of two-dimensional materials to choose from that all offer distinct features which can be exploited or their properties altered easily; additionally, van der Waals heterostructures can provide flexible materials with which tiny devices or structures may be created quickly and cost-effectively.
One of the hallmarks of 2D materials is their electrons being contained within one dimension but can move in both directions, providing for more precise control over gate voltage and possibly being resistant against shorter channel effects. Their bulk atomic scale may provide an alternative solution for quantum effects as well as more effective computer hardware designs.
2D materials offer computers the potential for increasing transistor dimensions to develop more efficient logic-based systems and memory-based computational operations based on matrix. They also present designers with an opportunity to increase memory performance while decreasing power consumption – and designers can take advantage of 2D materials to design these devices more effectively, leading to enhanced memory performance, lower power consumption, higher accuracy and scalability as well as the potential to overcome some issues associated with memory devices with large capacities.
There is increasing interest in using 2D matrix computing based on materials for AI applications, particularly neural networks. Memory cells must meet specific specifications; nonvolatile memory devices that utilize 2D materials provide an amplitude-specific encoder and may meet these needs; furthermore their combination with Ionic transistors creates an authentic implementation of biomimicry for neural network spiking applications.
At its core, 2D materials offer great promise as an emerging computing technology due to their unique properties that offer customization and flexibility. As technology improves, this could result in superior logic- and memory-based devices suitable for matrix computing or AI-oriented applications; and as the possibilities expand.
