AI transistors can process and store information at room temperature, similar to human brain thinking

IT House reported on December 23 that scientists have made another major breakthrough in the field of AI. An AI synaptic transistor that mimics the human brain has recently come out.

This AI synaptic transistor operates similarly to human cognition, with the ability to simultaneously process and store information, marking a significant shift from traditional machine learning tasks to performing associative learning

Please note that the source of the pictures below is: Northwest University, photos provided by Xiaodong Yan

The more important point is that this AI synaptic transistor can effectively operate in room temperature environment. It has the advantages of fast operation, low energy consumption, and the ability to retain information even without power supply. It has Broad application prospects.

The study was a collaboration between researchers at Northwestern University, Boston College and the Massachusetts Institute of Technology (MIT). In a press release, Professor Mark Hessam said:

The structure of the brain is essentially different from that of a digital computer

In a digital computer, data needs to be transferred back and forth between the microprocessor and memory, which inevitably consumes a lot of energy and can cause bottlenecks when trying to perform multiple tasks simultaneously

In the human brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude improvements in energy efficiency. Our synaptic transistors also enable concurrent memory and information processing functions to more faithfully imitate the brain

Hessam’s team mainly uses moiré patterns to develop synaptic transistors

IT Home Note: Moire pattern is a geometric pattern that appears when two patterns are superimposed on each other. When two-dimensional materials are stacked, new properties emerge that are not present in a single-layer structure. When these hierarchical structures are twisted to form Moiré patterns, it is possible to harvest unprecedented electronic properties

In this new synaptic transistor, the researchers combined two different types of atomically thin materials, double layers of graphene and hexagonal boron nitride. Although graphene and hexagonal boron nitride are very similar in structure, they are different enough to produce an unusually strong Moiré effect, allowing control of the electronic properties of the graphene layer

Operations that can be performed at room temperature and can generate synaptic transistors with enhanced neuromorphic functions

In the Lenovo Learning Test, this AI transistor can compare "101" and "111" and distinguish the latter from being more similar to "000"

This ability is important for artificial intelligence applied to the real world because it can handle complex and imperfect inputs

Attached is the reference address of the paper: IT Home

Yan, -023-06791-1

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