Saumil Joshi

Anatomy of Ag/Hafnia‐Based Selectors with 1010 Nonlinearity

A novel Ag/oxide-based threshold switching device with attractive features including ≈1010 nonlinearity is developed. High-resolution transmission electron microscopic analysis of the nanoscale crosspoint device suggests that elongation of an Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching.

A novel true random number generator based on a stochastic diffusive memristor

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things.Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching

Synaptic electronics and neuromorphic computing

In order to map the computing architecture and intelligent functions of the human brain on hardware, we need electronic devices that can emulate biological synapses and even neurons, preferably at the physical level. Beginning with the history of neuromorphic computation, in this article, we will briefly review the architecture of the brain and the learning mechanisms responsible for its plasticity. We will also introduce several memristive devices that have been used to implement electronic synapses, presenting some important milestones in this area of research and discussing their advantages, disadvantages, and future prospects.

Built-in selectors self-assembled into memristors

We demonstrate an approach to build a selector into ReRAM (memristors) using engineered materials. In this approach, a segment(s) of “nonlinear material” is self-assembled into the conduction channel (s) (filament) of a memristor. The nonlinear material exhibits a highly nonlinear current-voltage characteristic, which gives rise to a nonlinear i-v characteristic of the memristor in the ON state.