Memristive technologies are attractive due to their nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. For memristive devices, a model corresponding with practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. In this paper, we propose a novel memristor model based on the Ag/TiO x nanobelt/Ti configuration, which can reflect three different states (i.e. original stage, transition stage, and resistive switching state) of the physical memristor with a satisfactory fitting precision (greater than 99.88%). Meanwhile, this work gives (1) an insight onto the electrical characteristics of the memristor model under different humidity conditions; (2) the influence of the water molecular concentration on the memristor behavior, which is of importance for the memristor fabrication and subsequent applications. For verification purposes, the proposed three-state switchable memristor is applied into the memristor-based logic implementation. The experimental results demonstrate that the constructed circuit is able to realize basic Boolean logic operations with fast response speed and high efficiency.
A bioinspired in-sensing computing paradigm using emerging photoelectronic memristors pursues multifunctionality with low power consumption and high efficiency for processing large amounts of sensing information. An organic semiconductor memristor strategy based on the CuPc functional layer integrates a negative photoconductance (NPC) effect and an analogue switching memory (ASM) effect in the same pixel. The NPC effect, present in the pure capacitance state at low bias voltage, provides high-performance short/long-term synaptic plasticity modulable by light pulse parameters. The interface charge effect along with defeat site trapping and detrapping is responsible for the pure capacitance effect and the NPC effect, with electron tunneling and electric-field-driven band dynamics responsible for ASM. This work reveals an organic memristor approach for hardware implementation of a neuromorphic vision computing system, emulating retinal bipolar cells via light-dominated NPC and electrically induced ASM with stable, tunable conductance states.
Thanks to its structural characteristics and signal patterns similar to those of human brain synapses, memristors are widely believed to be applicable for neuromorphic computing. However, to our knowledge, memristors have not been effectively applied in the biomedical field, especially in disease diagnosis and health monitoring. In this work, a blood-based biomemristor was prepared for in vitro detection of hyperglycemia and hyperlipidemia. It was found that the device exhibits excellent resistance switching (RS) behavior at lower voltage biases. Through mechanism analysis, it has been confirmed that the RS behavior is driven by Ohmic conduction and ion rearrangement. Furthermore, the hyperglycemia and hyperlipidemia detection devices were constructed for the first time based on memristor logic circuits, and circuit simulations were conducted. These results confirm the feasibility of blood-based biomemristors in detecting hyperglycemia and hyperlipidemia, providing new prospects for the important application of memristors in the biomedical field.
Abstract With the advancement of artificial intelligence, optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing. Here, we disclose a flexible optomemristor based on C 27 H 30 O 15 /FeO x heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short- and long-term plasticity (STP and LTP), enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88% for speech recognition. The charge trapping and detrapping mediated by the optic active layer of C 27 H 30 O 15 that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor. This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing.
It is well-known that the reprogrammable device is one of the important needs for circuit design. In this paper, nanolayered TiO2 and maple leaves (ML) are combined to form a functional layer (TiO2-ML) inside memristive devices, which demonstrate both the capacitive effect and the nonvolatile storage capability. When the voltage increases from zero, the device first enters a capacitive-coupled memristive state at low voltage before switching to a normal memristive state at a higher voltage. The existence of the capacitive behavior results in a nonzero-crossing I–V characteristic different from the zero-crossing curve observed in normal memristive device. Utilizing this capacitive-coupled memristive behavior, we design a low power passive filter with applications toward reprogrammable analog circuit designs, paving a path toward a multifunctional nanodevice in the future.
Abstract Ion migration as well as electron transfer and coupling in resistive switching materials endow memristors with a physically tunable conductance to resemble synapses, neurons, and their networks. Four different types of volatile memristors and another four types of nonvolatile memristors are systemically surveyed in terms of the switching mechanisms and electrical properties that are the basis of different computing applications. The volatile memristor features spontaneous conductance decay after the cease of electrical/optical stimulations, which are closely related to the surface atom diffusion, metal–insulator–transition (including charge–density–wave), thermal spontaneous emission, and charge polarization. Such unique dynamic state evolution at the edge of chaos has enabled them to emulate certain synaptic and neural dynamics, leading to various applications ranging from spiking neural networks to combinatorial optimizations. Nonvolatile resistive switching behavior originated from the electron spins, ferroelectric polarization, crystalline‐amorphous transitions or interplay between ions and electrons enables the memristor array to implement the vector–matrix multiplication, which is the key convolutional operation in artificial neural networks. The progress, challenges, and opportunities for both volatile and nonvolatile memristor in the level of materials, integration technology, algorithm, and system are highlighted in this review.
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