We live in an analog world of continuous information flow that is both processed and stored by our brains at the same time, but our devices process information digitally in the form of discrete binary code, breaking the information into little bits (or bites). Researchers have revealed a pioneering technology that combines the potential of continuous analog processing with the precision of digital devices. By seamlessly integrating ultra-thin, two-dimensional semiconductors with ferroelectric materials, the research unveils a novel way to improve energy efficiency and add new functionalities in computing. The new configuration merges traditional digital logic with brain-like analog operations.
We live in an analog world of continuous information flow that is both processed and stored by our brains at the same time, but our devices process information digitally in the form of discrete binary code, breaking the information into little bits . Researchers at EPFL have revealed a pioneering technology that combines the potential of continuous analog processing with the precision of digital devices.
According to Professor Adrian Ionescu, head of Nanolab,"Our endeavors represent a significant leap forward in the domain of electronics, having shattered previous performance benchmarks, and is exemplified by the outstanding capabilities of the negative-capacitance tungsten diselenide/tin diselenide TFET and the possibility to create synaptic neuron function within the same technology."
Sadegh Kamaei, a PhD candidate at EPFL, has harnessed the potential of 2D semiconductors and ferroelectric materials within a fully co-integrated electronic system for the first time. The 2D semiconductions can be used for ultra-efficient digital processors whereas the ferroelectric material provides the possibility to continuously process and store memory at the same time. Combining the two materials creates the opportunity to harness the best of the digital and analog capacities of each.
Such advances hint at electronic devices that operate in ways parallel to the human brain, marrying computational speed with processing information in a way that is more in line with human cognition. For instance, neuromorphic systems might excel at tasks that traditional computers struggle with, such as pattern recognition, sensory data processing, or even certain types of learning.
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