An analog ac voltage amplifier based on a single straintronic magnetic tunnel junction

Magnetic tunnel junctions (MTJs) are known for their digital applications (memory and logic). A special class of them called "straintronic" magnetic tunnel junctions (s-MTJ) has lately emerged as a potential platform for analog applications because their conductance can be varied continuously with mechanical strain generated with a gate voltage. The conductance versus gate voltage (transfer) characteristic always has a linear region and that can be leveraged for a variety of analog applications. Here, we discuss one such application, namely analog voltage amplification. If the s-MTJ's gate voltage is fixed around the midpoint of the linear region and a small ac voltage is superimposed on it, then the ac voltage can be amplified without distortion as long as its amplitude is small enough to avoid gate voltage excursion beyond the linear region. Unlike a transistor-based voltage amplifier whose amplification is determined solely by the transistor's internal parameters - namely the transconductance and Early resistance - here the amplification can be varied by an external power supply voltage. We examine the maximum allowed amplitude and frequency of input signal for distortion-free amplification by modeling the magnetization dynamics and derive an expression for the amplification.

Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet

Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set. Here, we show how to implement a TSN with a zero-energy-barrier (shape isotropic) magnetostrictive nanomagnet subjected to uniaxial strain.