Signal amplification in simple metal-insulator transition devices
Signal dissipation in large-scale neural networks can lead to information loss and ultimately to computational failures, necessitating local signal amplification at neuron - synapse connections. In biological nervous systems, axons are responsible for local signal amplification. Translating axon functionality into hardware, i.e., the ability to amplify and transmit signals without loss, is non-trivial because emulating the human brain implies building networks composed of ~10 billion interconnected neurons, each requiring a dedicated compact and scalable amplifier. Here, we demonstrate signal amplification in simple two-terminal devices made of a metal-insulator transition material. By operating the devices on the verge of the phase transition and taking advantage of negative differential resistance, we achieve robust signal amplification up to a factor of ~11.5. We also demonstrate the amplification of spiking sequences generated by a real neuristor, opening new exciting opportunities for the direct integration of artificial neurons and axons. The amplification can be controlled by easily adjustable experimental parameters, including DC bias, AC excitation, series resistance, and temperature. We further propose a model that predicts the gain using readily observable transport characteristics. Our results establish a framework for developing and optimizing axon-like amplification functionalities in nonlinear electronic materials.