An efficient analog Hamming distance comparator realized with a unipolar memristor array: a showcase of physical computing

Ge, Ning; Yoon, Jung Ho; Hu, Miao; Merced-Grafals, Emmanuelle J.; Dávila, Noraica; Strachan, John Paul; Li, Zhiyong; Holder, Helen; Xia, Qiangfei; Williams, R. Stanley; Zhou, Xing; Yang, J. Joshua

doi:10.1038/srep40135

Cited by 31 publications

(16 citation statements)

References 36 publications

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“…Here, we report an artificial nociceptor built upon a diffusive memristor. As a two terminal ionic device that uses resistance states to represent information, memristor has been demonstrated for a wide variety of applications ranging from non-volatile memory 16 – 32 , bio-system emulators 33 – 36 , analog 37 , 38 , 39 and neuromorphic computing 40 – 46 , reconfigurable radiofrequency systems 47 , 48 to security applications 49 – 54 . The diffusive memristor 55 , which recently joined the family of memristive devices, is essentially a volatile threshold switching device with switching dynamics that has enabled direct emulation of biological synapse 55 and neurons, and can be utilized for other applications such as true random number generators 56 and physical unclonable functionalities as well.…”

Section: Introductionmentioning

confidence: 99%

An artificial nociceptor based on a diffusive memristor

et al. 2018

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A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the “no adaptation” and “sensitization” phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots.

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Section: Introductionmentioning

confidence: 99%

An artificial nociceptor based on a diffusive memristor

et al. 2018

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“…Memristor or resistive random access memory (RRAM) is a promising emerging device candidate for various computing applications such as neuromorphic computing, analog computing, physical computing, and next‐generation memory . Despite those remarkable demonstrations previously, ideal device performance with low switching voltage and current as well as good retention simultaneously in the same device, has rarely been achieved.…”

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confidence: 99%

A Low‐Current and Analog Memristor with Ru as Mobile Species

et al. 2020

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The switching parameters and device performance of memristors are predominately determined by their mobile species and matrix materials. Devices with oxygen or oxygen vacancies as the mobile species usually exhibit a great retention but also need a relatively high switching current (e.g., >30 µA), while devices with Ag or Cu as cation mobile species do not require a high switching current but usually show a poor retention. Here, Ru is studied as a new type of mobile species for memristors to achieve low switching current, fast speed, good reliability, scalability, and analog switching property simultaneously. An electrochemical metallization‐like memristor with a stack of Pt/Ta2O5/Ru is developed. Migration of Ru ions is revealed by energy‐dispersive X‐ray spectroscopy mapping and in situ transmission electron microscopy within a sub‐10 nm active device area before and after switching. The results open up a new avenue to engineer memristors for desired properties.

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“…The results show that both neuron "6" and neuron "5" fire when the input digit is "5" (or digit "6"), as well as neuron "8" and neuron "9" when input digit is "8" (or digit "9"). This is because the hamming distance between these patterns is small 55 , which results in nearly the same post-synaptic membrane potential (see Supplementary Fig. 10).…”

Section: Fully Hardware Multilayer Snnsmentioning

confidence: 95%

Hybrid memristor-CMOS neurons for in situ learning in fully hardware memristive spiking neural networks

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Wang

et al. 2020

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Spiking neural network, consisting of spiking neurons and plastic synapses, is a promising but relatively underdeveloped neural network for neuromorphic computing. Inspired by the human brain, it provides a unique solution for highly efficient data processing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in situ learning is very challenging. Here, a hybrid spiking neuron by combining the memristor with simple digital circuits is designed and implemented in hardware to enhance the neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, with which in situ Hebbian learning is achieved. This work opens up a way towards the implementation of spiking neurons, supporting in situ learning for future neuromorphic computing systems.

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An efficient analog Hamming distance comparator realized with a unipolar memristor array: a showcase of physical computing

Cited by 31 publications

References 36 publications

An artificial nociceptor based on a diffusive memristor

An artificial nociceptor based on a diffusive memristor

A Low‐Current and Analog Memristor with Ru as Mobile Species

Hybrid memristor-CMOS neurons for in situ learning in fully hardware memristive spiking neural networks

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