Nanoscale imaging of He-ion irradiation effects on amorphous TaOx_x toward electroforming-free neuromorphic functions

Resistive switching in thin films has been widely studied in a broad range of materials. Yet the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing amorphous TaO$_x$ as a model material system. Specifically, we apply Scanning Microwave Impedance Microscopy (sMIM) and cathodoluminescence (CL) microscopy as direct probes of conductance and electronic structure, respectively. These methods provide direct evidence of the electronic state of TaO$_x$ despite its amorphous nature. For example CL identifies characteristic impurity levels in TaO$_x$, in agreement with first principles calculations. We applied these methods to investigate He-ion-beam irradiation as a path to activate conductivity of materials and enable electroforming-free control over resistive switching. However, we find that even though He-ions begin to modify the nature of bonds even at the lowest doses, the films conductive properties exhibit remarkable stability with large displacement damage and they are driven to metallic states only at the limit of structural decomposition. Finally, we show that electroforming in a nanoscale junction can be carried out with a dissipated power of < 20 nW, a much smaller value compared to earlier studies and one that minimizes irreversible structural modifications of the films. The multimodal approach described here provides a new framework toward the theory/experiment guided design and optimization of electroresistive materials

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue‐memory‐based neuromorphic computing can be orders of magnitude more energy efficient at data‐intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer‐sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria‐stabilized zirconia (YSZ), toward eliminating filaments. Filament‐free, bulk‐RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk‐RRAM devices using TiO2‐X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk‐RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy‐efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.A resistive memory cell based on the electrochemical migration of oxygen vacancies for in‐memory neuromorphic computing is presented. By using the average statistical behavior of all oxygen vacancies to store analogue information states, this cell overcomes the stochastic and unpredictable switching plaguing filament‐forming memristors, and instead achieves linear, predictable, and deterministic switching.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/3/adma202003984_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/2/adma202003984-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/1/adma202003984.pd

High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network

Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation. Recent research has demonstrated memristors and spintronic devices in various neural network designs boost efficiency and speed. This paper presents a biologically inspired fully spintronic neuron used in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the results are compared against those of current Hopfield neuromorphic architectures which use emerging technologies