Voltage-Controlled Magnetism in Quantum Material Opens New Avenues for Neuromorphic Computing

Tuning Magnetism With Voltage Opens a New Path to Neuromorphic Circuits | Newswise

Applying a voltage to lanthanum strontium manganite (LSMO) can cause it to separate into distinct regions with dramatically different magnetic properties, as illustrated by the red and blue colors in the above figure. Lanthanum strontium manganite (LSMO) is a quantum material that is magnetic and conducts electricity at low temperature but is nonmagnetic and an insulator at room temperature. Quantum materials like LSMO are materials that possess special properties because of the rules of quantum mechanics. Researchers discovered that applying voltage to LSMO in its magnetic phase causes the material to split into regions with distinct magnetic properties. The magnetic properties of these regions depend on the applied voltage. This is important because normally, magnetic properties don't respond to voltage. However, in LSMO, voltage can be used to tune different magnetic regions in the same material. This breakthrough could lead to energy-efficient methods for controlling magnetism. Tuning a material's magnetism with an applied voltage is one way to develop circuits that mimic how the human brain processes information, also known as neuromorphic circuits. Another approach is tuning a material's resistance to switch from a low to a high value and vice versa. In LSMO, both resistance and magnetism can be tuned. This creates a new path to the realization of neuromorphic devices. These devices hold great potential for improving artificial intelligence, leading to smarter, faster, and more energy-efficient information processing technologies. A team of researchers has discovered a new way to control the magnetic behavior of quantum materials using applied voltages. Specifically, the material LSMO, which is magnetic and metallic at low temperatures but non-magnetic and insulating when relatively warm, can be influenced by voltage. The applied voltage causes the material to split into regions with different magnetic properties before it transitions from metallic and magnetic to insulating and non-magnetic. The researchers detected this phenomenon using a ferromagnetic resonance technique, which allows scientists to observe changes in the magnetic characteristics of LSMO under different voltage levels. In the ferromagnetic resonance technique, a peak is observed when the precession of the material's magnetization matches the frequency of an incoming electromagnetic wave. The experiments on LSMO measured multiple peaks, indicating that the material contained multiple magnetic phases. In each of these phases, the electron spins oscillated at a different frequency, producing different peaks. In addition, small changes in the applied voltage induced large changes in the oscillation frequencies. This result is important because it provides a path to improve the performance of neuromorphic circuits based on spin oscillator networks, also known as spintronic neuromorphic devices. In summary, the researchers discovered that LSMO is a material that can be used both for switching between high and low electrical resistance states and for spintronic applications, offering new possibilities for spintronic neuromorphic devices. This research was supported by the Quantum Materials for Energy Efficient Neuromorphic Computing, an Energy Frontier Research Center funded by the Department of Energy (DOE) Office of Science, Basic Energy Sciences.

Tuning magnetism with voltage opens a new path to spintronic neuromorphic circuits

A team of researchers has discovered a new way to control the magnetic behavior of quantum materials using applied voltages. Specifically, the material lanthanum strontium manganite (LSMO), which is magnetic and metallic at low temperatures but non-magnetic and insulating when relatively warm, can be influenced by voltage. The work is published in the journal Nano Letters. Quantum materials like LSMO are materials that possess special properties because of the rules of quantum mechanics. Researchers discovered that applying voltage to LSMO in its magnetic phase causes the material to split into regions with distinct magnetic properties. The magnetic properties of these regions depend on the applied voltage. This is important because normally, magnetic properties don't respond to voltage. However, in LSMO, voltage can be used to tune different magnetic regions in the same material. This breakthrough could lead to energy-efficient methods for controlling magnetism. Tuning a material's magnetism with an applied voltage is one way to develop circuits that mimic how the human brain processes information, also known as neuromorphic circuits. Another approach is tuning a material's resistance to switch from a low to a high value and vice versa. In LSMO, both resistance and magnetism can be tuned. This creates a new path to the realization of neuromorphic devices. The researchers detected this phenomenon using a ferromagnetic resonance technique, which allows scientists to observe changes in the magnetic characteristics of LSMO under different voltage levels. In the ferromagnetic resonance technique, a peak is observed when the precession of the material's magnetization matches the frequency of an incoming electromagnetic wave. The experiments on LSMO measured multiple peaks, indicating that the material contained multiple magnetic phases. In each of these phases, the electron spins oscillated at a different frequency, producing different peaks. In addition, small changes in the applied voltage induced large changes in the oscillation frequencies. The researchers discovered that LSMO is a material that can be used both for switching between high and low electrical resistance states and for spintronic applications, offering new possibilities for spintronic neuromorphic devices. This result is important because it provides a path to improve the performance of neuromorphic circuits based on spin oscillator networks, also known as spintronic neuromorphic devices. These devices hold great potential for improving artificial intelligence, leading to smarter, faster, and more energy-efficient information processing technologies.