Artificial Astrocyte Memristor with Recoverable Linearity for Neuromorphic Computing

Abstract

Neuromorphic systems provide a potential solution for overcoming von Neumann bottleneck and realizing computing with low energy consumption and latency. However, the neuromorphic devices utilized to construct the neuromorphic systems always focus on artificial synapses and neurons, and neglected the important role of astrocyte cells. Here, an astrocyte memristor is demonstrated with encapsulated yttria‐stabilized zirconia (YSZ) to emulate the function of astrocyte cells in biology. Due to the high oxygen vacancy concentration and resultant high ionic conductivity of YSZ, significantly lower forming and set voltages are achieved in the artificial astrocyte, along with high endurance (>1011). More importantly, the nonlinearity in current‐voltage characteristics that usually emerge as the testing cycle increases can be depressed in the astrocyte memristor, and the nonlinearity can also be reversed by applying a refresh operation, which implements the role of biological astrocyte in maintaining the normal activity of neurons. The recovery of linearity can dramatically improve the accuracy of Modified National Institute of Standards and Technology dataset classification from 62.98% to 94.75% when the inputs are encoded in voltage amplitudes. The astrocyte memristor in this work with improved performance and linearity recovery characteristics can well emulate the function of astrocyte cells in biology and have great potential for neuromorphic computing.

Neuromorphic Synapses with High Switching Uniformity and Multilevel Memory Storage Enabled through a Hf-Al-O Alloy for Artificial Intelligence

“…8−10 To put it another way, the electrode media and materials have a big impact on memristor performance features including changeable conductivity and retention. 11,12 As a way to develop electrical synapses, the development of memristive devices with progressively modifying conductivity and a great lifespan is considered to develop electrical synapses. Synapses in the brain have established a wide range of memory lifetimes, ranging from a few seconds to decades.…”

“…Memristor-based neuromorphic systems challenge conventional computers because of their basic combination of storage and computation. , In terms of structure and behavior, memristive devices have properties similar to biological neuromorphic synapses, making them promising contenders for simulating the switching synaptic weights in biological approaches, as their conductivity progressively adjusts with the switch in charge or flow. − The concentration of neurotransmitters at biological synapses weakens or strengthens the connection between neurons. Correspondingly, the movement of cations or vacancies linked with the medium and electrode materials causes potentiation and depression plasticity in memristors. − To put it another way, the electrode media and materials have a big impact on memristor performance features including changeable conductivity and retention. , As a way to develop electrical synapses, the development of memristive devices with progressively modifying conductivity and a great lifespan is considered to develop electrical synapses. Synapses in the brain have established a wide range of memory lifetimes, ranging from a few seconds to decades.…”

Trade-off between Gradual Set and On/Off Ratio in HfOx-Based Analog Memory with a Thin SiOx Barrier Layer

“…The linearity in the I – V curve of non-volatile memory devices is crucial for precisely reading the current, which determines the results of an arithmetic operation in cross-point RRAM array . Prior studies have directly correlated the importance of I – V linearity with improved accuracy in image classification during neural network training . In addition, compared to HfO x devices, the HfO x /SiO x devices show less abrupt resistance change at a high compliance level (∼1 mA I cc ), as shown in Figure S2a.…”

Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces

“…As one of the hot technologies in the world's cutting-edge scientific research, artificial intelligence and brain-computer biological tissues. [12][13][14][15][16][17][18] Thus, bio-voltage memristor is expected to be implemented in low power neuromorphic computing, brain-computer interfaces, and biological integration interfaces. Memristors have a simple sandwich structure consisting of a top electrode (TE), an active layer, and a bottom electrode (BE) (as illustrated in Figure 1d).…”