- Artificial intelligence may work on a similar principle to the human brain
- Moire patterns can change how transistors work
- Graphene and hexagonal boron nitride produce strong moiré effects
In the ever-evolving landscape of artificial intelligence, scientists and researchers are focused on developing computer systems that mimic the complexities of the human brain. Recent advances in artificial intelligence have spurred a collective exploration of ways to make computers more efficient, especially when processing the vast amounts of data generated by smart devices.
The joint efforts of experts from the Massachusetts Institute of Technology (MIT), Northwestern University and Boston College have produced a remarkable breakthrough – the creation of a specialized transistor designed to mimic the cognitive functions of the human brain. Unlike traditional computers that consume significant amounts of energy, this innovative transistor aims to revolutionize the efficiency of computing processes.
Will this transistor behave similarly to the human brain?
This unique transistor is characterized by remarkable performance at room temperature, which is a significant difference from other devices that try to work on a similar principle to the human brain. In addition to temperature adaptability, the transistor boasts impressive speed, energy efficiency, and the ability to store information even when not actively powered.
Mark C. Hersam, co-leader of the research, emphasized the fundamental difference in the architecture of the human brain and the digital computer. He pointed out that in a computer, data constantly travels between the microprocessor and memory, which leads to significant power consumption and creates multitasking bottlenecks.
Why do we need to change the approach to transistors?
For decades, the electronics paradigm revolved around using transistors within the same silicon architecture, Hersam explained. Significant progress has been made by constantly increasing the number of transistors integrated into circuits.
Computer motherboard (illustrative image)
While acknowledging the success of this strategy, Hersam highlighted its downsides, primarily increased energy consumption, especially in the current era of big data, where digital computing is straining power grids. The implications of this approach are particularly significant in the field of artificial intelligence and machine learning, which creates a fundamental need to rethink computing hardware. Hersam emphasized the need to rethink conventional computing methodologies to meet the evolving demands of AI and machine learning tasks.
What are moiré patterns?
Moiré is a fascinating visual phenomenon that occurs when two or more repeating patterns overlap with a slight offset. The name is derived from the French word for “connected”, which aptly describes the complex and undulating appearance of these patterns. The concept of moiré has deep roots in mathematics and physics, where they are studied as interference patterns. Interference occurs when two or more waves, such as light or sound, combine to create a new pattern. In the case of moiré patterns, the waves are the repeating elements of overlapping patterns.
Visual characteristics of moiré patterns
Moiré patterns are characterized by a series of light and dark stripes that are created by the interaction of basic patterns. The specific appearance of a moiré pattern depends on several factors, including the spacing of the patterns, their relative orientation, and the viewing angle.
Where can we meet them? Moiré patterns are ubiquitous in our daily lives. Imagine you want to take a picture with regular patterns, for example on wallpaper. If you take a photo of such wallpaper, it may happen that strange and unnatural patterns appear in the image that are not actually on the wallpaper. This phenomenon, similar to the moiré effect, can be created by the interaction between the structure of the pattern on the wallpaper and the resolution of the camera, which can distort the original visual impression.
How can we use it? An example can be moiré quantum materials, which are characterized by enhanced internal Coulomb interactions in two-dimensional heterostructures. Heterostructures are materials composed of two or more different materials that are systematically arranged at the microscopic level. In the context of our topic, this means that heterostructures are two planar layers systematically crossed or rotated relative to each other. We can call this specific arrangement a moiré structure, which has great potential for revealing new exotic electronic phenomena. Coupled with the remarkable electrostatic control achievable in atomically thin materials, moiré heterostructures appear to be promising candidates for introducing a new generation of electronic devices. However, despite extensive research, the use of moiré quantum materials is hindered by their confinement to impractically low cryogenic temperatures, which limits their applicability in real-world scenarios.
How does it all work?
In their groundbreaking research, the researchers harnessed the potential of moiré patterns, unique patterns that are key to manipulating superthin materials. By folding and twisting these materials, they created moiré patterns that gave them distinct electronic properties. The twist angle turned out to be a key factor that allowed scientists to tailor the electronic properties to specific needs. The result was the development of an innovative device, the synaptic transistor, which exhibits brain-like functionality even at room temperature.
Dr. Hersam, a key figure in this research, emphasized the importance of twist angle as a new design parameter that unlocks a wide range of permutations. Graphene and hexagonal boron nitride, although structurally similar, differ just enough to induce remarkably robust moiré effects, contributing to the unique electronic properties observed.
Model of graphene structure
Dr. Hersam and his team set out to train the synaptic transistor to recognize complex patterns. They started with a basic pattern such as 000 and challenged the device to recognize analogous patterns such as 111 or 101. The results were astounding, the device not only successfully recognized these patterns but also exhibited a sophisticated form of learning known as associative learning. Even when presented with incomplete patterns, the synaptic transistor demonstrated robust functionality, demonstrating its potential for versatile and adaptive applications. This achievement pushes the field of moiré quantum materials into a new realm of possibilities and promises advanced functions for future electronic devices.
The researchers stressed the importance of moving AI technology beyond simple sorting tasks as part of the effort to develop artificial intelligence (AI) systems that mimic higher-level cognitive functions, rather than just basic data classification. While data classification involves sorting information into different bins, researchers aim to elevate AI capabilities towards more sophisticated cognitive processes.
In addition, the unique properties of moiré synaptic transistors open up possibilities for applications in edge computing and artificial neural networks. The adaptability that the synaptic transistor has demonstrated in pattern recognition and learning positions it as a key player in the development of artificial intelligence technology. The potential benefits extend to scenarios where real-time decision-making in response to dynamic changes in the environment is key, and offer a glimpse into a future where AI systems exhibit a finer and more human-like understanding of complex situations.
Author of the article
Josef Novak
I am a PhD student dealing with ion applied technologies, because I have always been fascinated by science and technology. I never cease to be amazed at what can be created thanks to human creativity and abilities. I like to spend my free time traveling, either in the mountains or in the cities.
2023-12-30 11:00:00
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