B. N. Kurdi

TL;DR: In this article, the authors survey the current state of phase change memory (PCM), a nonvolatile solid-state memory technology built around the large electrical contrast between the highly resistive amorphous and highly conductive crystalline states in so-called phase change materials.

TL;DR: Using 2 phase-change memory devices per synapse, a 3-layer perceptron network is trained on a subset of the MNIST database of handwritten digits using a backpropagation variant suitable for NVM+selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2%.

TL;DR: In this article, the authors review the candidate solid-state nonvolatile memory technologies that potentially could be used to construct a storage-class memory (SCM) and compare the potential for practical scaling to ultrahigh effective areal density for each of these candidate technologies.

TL;DR: This work discusses evolutionary extensions of conventional flash memory, such as SONOS and nanotraps, as well as a number of revolutionary new memory technologies, including ferroelectric, magnetic, phase-change, and resistive random-access memories, including perovskites and solid electrolytes, and finally organic and polymeric memory.

TL;DR: It is shown that a bidirectional NVM with a symmetric, linear conductance response of high dynamic range is capable of delivering the same high classification accuracies on this problem as a conventional, software-based implementation of this same network.