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We show resistive switching effects in memristive devices exhibit significant stochasticity. When the switching is dominated by a single filament, the switching time is fully random and shows a broad distribution. However, the switching distribution can be predicted and responds well to controlled changes in the programming conditions. The native stochastic characteristic can be used to generate random...
A two-terminal memristor device is a promising digital memory for its high integration density, substantially lower energy consumption compared to CMOS, and scalability below 10 nm. However, a nanoscale memristor is an inherently stochastic device, and extra energy and latency are required to make a deterministic memory based on memristors. Instead of enforcing deterministic storage, we take advantage...
This paper describes how memristors, together with CMOS transistors (CMOS-memristor hybrids), could be used for analog arithmetic within cellular (locally connected, regular) computing arrays, i.e., cellular nanoscale networks (CNN). Elementary analog programming of memristors and copying memristance values are described. Also, we show how memristors could be used for addition, subtraction, multiplication...
We describe a generic exponential model with four parameters for thin-film memristive devices. This model is used to analyze the time dependency of the threshold voltage which defines the transition between non-programming and programming phases of the device. A relationship between timescale of operation and threshold voltage is derived. Furthermore, self-terminating programming is considered using...
We report studies on nanoscale Si-based memristive devices for memory and neuromorphic applications. The devices are based on ion motion inside an insulating a-Si matrix. Digital devices show excellent performance metrics including scalability, speed, ON/OFF ratio, endurance and retention. High density non-volatile memory arrays based on a crossbar structure have been fabricated and tested. Devices...
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