High density crossbar arrays with sub- 15 nm single cells via liftoff process only

Emerging nano-scale technologies are pushing the fabrication boundaries at their limits, for leveraging an even higher density of nano-devices towards reaching 4F2/cell footprint in 3D arrays. Here, we study the liftoff process limits to achieve extreme dense nanowires while ensuring preservation of thin film quality. The proposed method is optimized for attaining a multiple layer fabrication to reliably achieve 3D nano-device stacks of 32?×?32 nanowire arrays across 6-inch wafer, using electron beam lithography at 100?kV and polymethyl methacrylate (PMMA) resist at different thicknesses. The resist thickness and its geometric profile after development were identified to be the major limiting factors, and suggestions for addressing these issues are provided. Multiple layers were successfully achieved to fabricate arrays of 1 Ki cells that have sub- 15?nm nanowires distant by 28?nm across 6-inch wafer

An RRAM biasing parameter optimizer

Research on memory devices is a highly active field, and many new technologies are being constantly developed. However, characterizing them and understanding how to bias for optimal performance are becoming an increasingly tight bottleneck. Here, we propose a novel technique for extracting biasing parameters, conducive to desirable switching behavior in a highly automated manner, thereby shortening the process development cycles. The principle of operation is based on: 1) applying variable amplitude, pulse-mode stimulation on a test device in order to induce switching multiple times; 2) collecting the data on how pulsing parameters affect the device’s resistive state; and 3) choosing the most suitable biasing parameters for the application at hand. The utility of the proposed technique is validated on TiOx-based prototypes, where we demonstrate the successful extraction of biasing parameters that allow the operation of our devices both as multistate and binary resistive switches

Emulating long-term synaptic dynamics with memristive devices

The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS technology, the properties of the memristive material should be harnessed in the context of such computation, under the view that biological synapses are memristors. Here we demonstrate that single solid-state TiO2 memristors can exhibit associative plasticity phenomena observed in biological cortical synapses, and are captured by a phenomenological plasticity model called triplet rule. This rule comprises of a spike-timing dependent plasticity regime and a classical hebbian associative regime, and is compatible with a large amount of electrophysiology data. Via a set of experiments with our artificial, memristive, synapses we show that, contrary to conventional uses of solid-state memory, the co-existence of field- and thermally-driven switching mechanisms that could render bipolar and/or unipolar programming modes is a salient feature for capturing long-term potentiation and depression synaptic dynamics. We further demonstrate that the non-linear accumulating nature of memristors promotes long-term potentiating or depressing memory transitions

Stochastic switching of TiO2 based memristive devices with identical initial memory states

In this work, we show that identical TiO2-based memristive devices that possess the same initial resistive states are only phenomenologically similar as their internal structures may vary significantly, which could render quite dissimilar switching dynamics. We experimentally demonstrated that the resistive switching of practical devices with similar initial states could occur at different programming stimuli cycles. We argue that similar memory states can be transcribed via numerous distinct active core states through the dissimilar reduced TiO2-x filamentary distributions. Our hypothesis was finally verified via simulated results of the memory state evolution, by taking into account dissimilar initial filamentary distribution