Brain-like Transistor Mimics Human IQ
Researchers just developed a new synaptic transistor capable of higher-level thinking. Researchers at Northwestern University, Boston College, and the Massachusetts Institute of Technology (MIT) innovated a device that simultaneously processes and stores information just like a human brain. They demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and can perform associative learning.
Previously, similar transistors could not function outside cryogenic temperatures. The new device is stable at room temperature, operates fast, consumes little energy, and retains stored information even when power is removed.
In a digital computer, data moves back and forth between a microprocessor and memory. Memory and information processing in the brain are co-located and fully integrated for orders of magnitude higher energy efficiency. The synaptic transistor achieves concurrent memory and information processing functionality, mimicking the brain more faithfully.
Recent advances in AI have motivated researchers to develop computers that operate more like the human brain. Smart devices continuously collect vast quantities of data, and researchers are scrambling to uncover new ways to process it without increasing the power needed to do so. Currently, the memory resistor, or “memristor,” is the most well-developed technology that can perform combined processing and memory functions. But memristors still suffer from high-energy switching.
The researchers combined two types of atomically thin materials for the new device: bilayer graphene and hexagonal boron nitride. When stacked and purposefully twisted, the materials formed a moiré pattern. Rotating one layer relative to the other, researchers could achieve different electronic properties in each graphene layer even when separated by only atomic-scale dimensions.
To test the transistor, Hersam and his team trained it to recognize similar — but not identical — patterns. Just earlier this month, Hersam introduced a new nanoelectronic device capable of analyzing and categorizing data in an energy-efficient manner, but his new synaptic transistor takes machine learning and AI one leap further. The researchers showed the device one pattern, 000, and asked the AI to identify similar patterns, such as 111 or 101.
The new synaptic transistor successfully recognized similar patterns in experiments, displaying its associative memory. Even when the researchers threw curveballs — like giving it incomplete patterns — it still successfully demonstrated associative learning.
