A novel computing paradigm called neuromorphic computing imitates the essential synaptic functions of neurons to simulate brain behavior. Neuronal plasticity, connected to learning and memory, is one of these functions. This plasticity allows neurons to store information or forget it depending on the length and frequency of electrical impulses that activate them.
Memresistive materials, ferroelectrics, phase change memory materials, topological insulators, and, more recently, magneto-ionic materials stand out among the materials that resemble neuron synapses. In the latter, applying an electric field causes the ions to be displaced within the material, changing the substance’s magnetic characteristics.Â
Although the modulation of magnetism in these materials when an electric field is applied is well understood, it is challenging to control the evolution of magnetic characteristics when voltage is ceased (i.e., the evolution following the stimulus). This makes it difficult to replicate some brain-inspired processes, such as keeping learning effectively even while the brain is in a deep sleep state (i.e., without external stimulation).
In a new study, scientists from the UAB Department of Physics Jordi Sort and Enric Menéndez, in collaboration with the ALBA Synchrotron, the Catalan Institute of Nanoscience and Nanotechnology (ICN2), and the ICMAB, proposed a new way to control the evolution of magnetization both in the stimulated and in the post-stimulus states.
They have developed a magnetic material capable of imitating the way the brain stores information. Thanks to this material, it is possible to imitate the synapses of neurons and mimic, for the first time, the learning that occurs during deep sleep.
Scientists developed the material based on a thin layer of cobalt mononitride (CoN) where, by applying an electric field, the accumulation of N ions at the interface between the layer and a liquid electrolyte in which the layer has been placed can be controlled.
ICREA research professor Jordi Sort and Serra Húnter Tenure-track Professor Enric Menéndez said, “The new material works with the movement of ions controlled by electrical voltage, in a manner analogous to our brain, and at speeds similar to those produced in neurons, of the order of milliseconds. We have developed an artificial synapse that in the future may be the basis of a new computing paradigm, alternative to the one used by current computers.”
By applying voltage pulses, it has been possible to emulate, in a controlled way, processes such as memory, information processing, information retrieval, and, for the first time, the controlled updating of information without applied voltage.
The cobalt mononitride layer thickness, which controls how quickly the ions move, and the pulse frequency were changed to accomplish this control.
The arrangement of the material allows the magnetoionic properties to be controlled not only when the voltage is applied but also, for the first time, when the voltage is removed. Once the external voltage stimulus disappears, the magnetization of the system can be reduced or increased, depending on the thickness of the material and the protocol of how the voltage has been previously applied.
A wide range of new neuromorphic computing functions is now possible because of this novel result. It provides a novel logic function that, for instance, makes it possible to simulate neuronal learning following brain stimulation while we deeply sleep. Other kinds of neuromorphic materials currently on the market cannot replicate these capabilities.
Jordi Sort and Enric Menendez said, “When the thickness of the cobalt mononitride layer is below 50 nanometers, and with a voltage applied at a frequency greater than 100 cycles per second, we have managed to emulate an additional logic function: once the voltage is applied, the device can be programmed to learn or to forget, without the need for any additional input of energy, mimicking the synaptic functions that take place in the brain during deep sleep, when information processing can continue without applying any external signal.”
Journal Reference:
- Zhengwei Tan, Julius de Rojas, Sofia Martins, Aitor Lopeandia, Alberto Quintana, Matteo Cialone, Javier Herrero-MartÃn, Johan Meersschaut, André Vantomme, José L. Costa-Krämer, Jordi Sort, Enric Menéndez. Frequency-dependent stimulated and post-stimulated voltage control of magnetism in transition metal nitrides: towards brain-inspired magneto-ionics. Materials Horizons, 2022. DOI: 10.1039/D2MH01087A