Memristive Switching Research Articles

Electrohydrodynamically Printed Flexible Organic Memristor for Leaky Integrate and Fire Neuron

Polymer memristors with good flexibility are promising electronic devices for edge computing paradigms in wearable electronics. However, most reported works present nonvolatile devices, which are suitable for applications in resistive random-access memory or artificial synapse. The volatile devices, which are indispensable in artificial neuron circuits, have rarely been reported, as the resistive switching property of organic devices is usually unstable in volatile devices. Moreover, most reported works use polymers in a way that is not compatible with traditional memristor fabrication technology, which limits the further applications of polymer memristors to large-scale neuromorphic circuits. In this letter, an Ag/Nafion/Au threshold switching memristor was fabricated via electrohydrodynamic printing technology and employed as a core of a leaky integrate and fire neuron circuit. The threshold switching memristor shows excel device size, good endurance, good device-to-device and cycle-to-cycle uniformity, flexibility, and stability at high temperatures. The combination of electrohydrodynamic printing technology and polymer memristor is promising in fast memristor production and largescale edge-computation network circuits.

Plasmonic Optoelectronic Memristor Enabling Fully Light-Modulated Synaptic Plasticity for Neuromorphic Vision.

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO2 nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision.

Resistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability.

Ion-based memristive switching has attracted widespread attention from industries owing to its outstanding advantages in storage and neuromorphic computing. Major issues for achieving brain-inspired computation of highly functional memory in redox-based ion devices are relatively large variability in their operating parameters and limited cycling endurance. In some devices, volatile and nonvolatile operations often replace each other without changing operating conditions. To address these issues, it is important to observe directly what is happening in repeated operations. Herein, we use a planar device that enables direct capturing of microscopic behaviors in the nucleation and growth of metal whiskers under repeated switching to verify the microscopic origin of the large parameter variability. We report direct observations that reveal the physical origin for the large cycle-to-cycle and device-to-device variability in memristive switching, which was achieved using planar polymer atomic switches with a gap >1 μm. We find that the deposition location of metal atoms is closely related to the crystallinity of the ion transport layer (solid polymer electrolyte, SPE). The filament variability (shape, position, quantity, etc.) during different cycles and devices is indeed the main reason for the observed variability in the operating characteristics. The results shed unique light on the complexity of the operation of the ion device, that is, the evolution of the dielectric layer and metal filament must be considered.

Donor-acceptor-type poly[chalcogenoviologen-alt-triphenylamine] for synaptic biomimicking and neuromorphic computing

SummaryPolymer memristors are preeminent candidates for low-power edge computing paradigms. Poly[chalcogenoviologen-alt-triphenylamine] (PCVTPA) has been synthesized by direct coupling of chalcogeno-viologen as electron acceptor and 4-(bromomethyl)-N-(4-(bromo-methyl)phenyl)-N-phenylaniline as electron donor. The introduction of chalcogen atoms (S, Se, Te) into viologen scaffolds can greatly improve electrical conductive, electrochemical, and electrochromic properties of the materials when compared with the conventional viologens. Taking PTeVTPA as an example, the as-fabricated electronic device with a configuration of Al/PTeVTPA/ITO exhibits excellent multilevel storage and history-dependent memristive switching performance. Associated with the unique memristive behavior, the PTeVTPA-based device can not only be used to emulate the synaptic potentiation/depression, the human's learning and memorizing functions, and the transition from short-term synaptic plasticity to long-term plasticity but also carry out decimal arithmetic operations as well. This work will be expected to offer a train of new thought for constructing high-performance synaptic biomimicking and neuromorphic computing system in the near future.

A neuromorphic core based on threshold switching memristor with asynchronous address event representation circuits

A neuromorphic core based on threshold switching memristor with asynchronous address event representation circuits

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO2 has been accepted as one of the most promising resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO2-based memristors. Here, the conductive filament system in the amorphous HfO2-based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf6O and its crystalline surroundings (monoclinic or tetragonal HfOx). The phase of the HfOx shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO2 is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO2-based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications.

Advancing in-memory Arithmetic Based on CMOS-integrable Memristive Crossbar Structures

Memristive computing will be advantageous in large-scale, highly parallel mixed-mode processing architectures because processing can be performed directly within memristive memory architectures and intrachip communication can be implemented by a memristive crossbar structure with reconfigurable logic gates. Here we report on the development of a new concept for in-memory adders, using XOR functionality. Exploited memristive crossbar structures are based on memristive complementary resistive switches, e.g. TaOx, and BiFeO3.

Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system

Multilevel operation is one of the most essential properties for synaptic devices to realize hardware artificial neural networks. Compliance current (Icc) adjustment is a multilevel programming method that can be utilized for a large-scale one-transistor and one-resistor (1T1R) array. It protects the devices from permanent breakdown by regulating abrupt switching. However, according to the reported literature so far, the number of conductance states in the Icc control method is insufficient to implement off-chip-trained neuromorphic systems. Therefore, we experimentally explore the feasibility of a larger number of conductance states using the Icc control method. We fabricated an Al2O3/TiOx-based resistive switching memory array, observed the conductance change while increasing Icc during set operations, and 64-level conductance states were statistically demonstrated. Furthermore, we verified that the 64-level states showed recognition performance close to that of a software-based neural network through off-chip learning of the convolutional neural network (CNN) structure. The fabricated synaptic device array with the Icc-control programming method is expected to contribute to the development of hardware neural network by reducing the information loss in the transfer process.

A Physically Transient Self-Rectifying and Analogue Switching Memristor Synapse

A physical transient memristor synapse with self-rectifying and analogue switching behaviors based on Mo/MgO/AZO (Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> 2 wt %, ZnO 98 wt %)/W is presented. By modulating thickness of MgO layer, the device with 5 nm MgO insulator layer shows stable resistive switching memory with a rectification ratio up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and precise tuning synaptic functions. Meanwhile, the formation of Schottky barrier and oxygen vacancy conductive filament is proposed to further explain self-rectifying and analogue switching behaviors. Additionally, the electrical characteristics of the Mo/MgO/AZO/W devices degraded after being immersed in deionized water (DI) for 3 min, which demonstrated the physically transient features successfully. The physical transient self-rectifying memristors have great potential for applications in secure neuromorphic computing systems, and bio-integrated electronics.

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors.

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

AbstractSpiking encoded stochastic neural network is believed to be energy efficient and biologically plausible and an increasing effort has been made recently to translate its great cognitive power into hardware implementations. Here, a stacked indium–gallium–zinc–oxide (IGZO)‐based threshold switching memristor with essential properties as a spiking stochastic neuron is introduced. Such IGZO spiking stochastic neuron shows a sigmoid firing probability that can be tuned by the amplitude, width, and frequency of the applied pulse sequence. More importantly, the stacked configuration is experimentally demonstrated with eliminated switching variation compared to one single memristor and a narrow relative deviation (≤6.8%) of the firing probability can be achieved. The IGZO stochastic neuron is applied to perform probabilistic unsupervised learning for handwritten digit reconstruction based on a restricted Boltzmann machine and a recognition accuracy of 91.2% can be achieved. Such IGZO stochastic neuron with reproducible firing probability emulates probabilistic computing in the brain, which is of significant importance to hardware implementation of spiking neural network to analyze sensory stimuli, produce adequate motor control, and make reasonable inference.

(Invited) ALD Based Flexible Memristive Synapses for Neuromorphic Computing Application

With the gradual development of artificial intelligence devices, the demand for data-intensive intelligent computation is increasing. The classical von Neumann computing scheme is suffering a bottleneck of complex information processing and storage[1]. In order to improve computing efficiency, In-memory Computing architectures were proposed to complete computing tasks based on storage units, including neuromorphic computing and logic-in-memory. On one hand, neuromorphic computing has become an attractive candidate due to the high efficiency by emulating the computing mode of human brain[2]. On the other hand, in-situ Boolean logic calculations can combine the arithmetic logic unit (ALU) and the storage unit to form memory-ALU (MALU), which could use the storage states of the memristor as the logic states to implement 16 basic Boolean logic gates[3]. Memristors with continuously adjustable conductance induced by voltage stimulation are suitable for simulating the weights update of bio-synapse[4]. Firstly, the ALD flexible artificial neural network (ANN) based on the integration of three-dimensional (3D) memristors network was proposed. Based on the structure of Pt/HfAlOx/TaN, the 3D ANN not only exhibits excellent memristive switching characteristics, but also achieves ultralow power synaptic characteristic (4.28 aJ/spike) under spike of 50 ns. The operation speed is 5 orders of magnitude higher than that of biological synapses, and the power consumption is 2-4 orders of magnitude lower than that of biological synapses. Besides, the 3D ANN has realized the function of information transmission including single channel and multi-channel, which could successfully transfer the letters “F”, “F” and “U” between the synapses of multilayer nerves. For the "0-9" digital images (28×28 pixels) in the MNIST database, the 3D ANN can complete the recognition processing, and the recognition rate can reach 88.8%. Finally, the in-memory computing device that integrates brain-like analog neuromorphic computing and digital logic computing was prepared, and its functions in low-power storage, brain-like neuromorphic computing, and in-situ Boolean logic computing were studied. In terms of logic calculation, the device can implement Boolean logic gates such as IMP, FALSE, and NAND by the combination and transitions of storage states, laying the foundation for its application in a multi-data type integrated in-memory computing system. This work provides effective guidelines for the development of ALD based devices for low power consumption in-memory computing system.

Chemical Vapor Deposition of MoS2 for Back-End-of-Line Applications

I.Introduction2D transition metal dichalcogenides (TMDs) exhibit electronic properties from semimetals to wide bandgap semiconductors due to their thickness dependent bandgap. This property opens a wide diversity of applications that can be made from TMDs. For 3D heterogeneous integration of TMDs into the back-end-of-line (BEOL) of Si complementary metal-oxide-semiconductor circuitry, wafer-level direct growth (without film transfer) of TMDs within the BEOL thermal budget limit (550 °C/2 hours or 500 °C/5 hours [1]) is essential. In the literature, the majority of the TMDs (e.g. MoS2) are achieved from high temperature (650 °C - 1100 °C) deposition/anneal processes that are not compatible with BEOL [2]. In this work, MoS2 is successfully grown by chemical vapor deposition (CVD) approximately at/below BEOL thermal budget limit. We explore the potential of the achieved MoS2 films for BEOL logic (i.e. transistors), memory and sensing applications.II. CVD of MoS2 MoS2 was grown by CVD in a commercial 300 mm atomic layer deposition (ALD) reactor fitted with a showerhead. Mo(CO)6 (purity 99.99%, carried by N2) and H2S (purity 99.999%, 1% in Ar) were employed as the precursors. The reactor temperature was restricted at a low temperature range of 350 °C - 550 °C and the chamber pressure was maintained at ∼2.2 Torr. Growth was achieved (confirmed by Raman spectroscopy) on a selection of substrates including: SiO2 (85 nm) on Si (p++), c-plane sapphire, glass, and amorphous alumina (~30 nm by ALD on c-plane sapphire and on glass). It is noted that cm-scale substrates were placed throughout the chamber demonstrating that uniform and continuous film deposition is achievable across a 300 mm diameter with a high level of repeatability.III. Results and DiscussionHigh-resolution cross-sectional transmission electron microscopy (TEM) shows that 2D layered and polycrystalline MoS2 of ~10 nm is formed continuously and uniformly on a SiO2/Si substrate at 550 °C for 2.5 hours. Plan-view TEM also demonstrates the hexagonal structures of the MoS2. A rapid growth rate of ∼6 monolayers/hour on SiO2/Si is remarkable. Growth rate is slightly different depending on the substrates used. A rapid growth time is important in terms of reducing the thermal budget, but this comes at a cost of small grain sizes that are ~5 nm - 20 nm from this work, compared to μm-scale grain size achieved at 550 °C in the CVD of MoS2 by Kang et al., in which 26 hours was needed for 1 monolayer formation [3]. Due to the small grain size, charges are trapped at the high density of grain boundaries restricting lateral charge transport which is detrimental to transistors. This effect is also demonstrated in our work by 4-point resistivity and Hall-effect measurements, which show that the MoS2 films are highly resistive with a very low carrier concentration of 1014 - 1015 cm-3.Furthermore, MoS2 formation is achieved at 450 °C and 350 °C. The reduction in growth temperature from 550 °C, which is at the limit of the maximum temperature of the BEOL thermal budget limit [1], is also a crucial step (apart from reducing growth time) towards achieving 3D heterogeneous integration as this allows room for more manufacturing steps, for example, a dopant incorporation/activation step to functionalize TMDs for transistors.Although MoS2 grain boundaries and stoichiometric defects resulting from the low thermal budget growth are detrimental to lateral charge transport in transistors, these can be leveraged for memory and sensing functions. For memory, vertical transport memristor structures (Au/MoS2/Au) incorporating ~3 nm MoS2 (550 °C, 0.75 hour) show memristive switching and a stable memory window of 105 between the high-low resistive states with a retention time >104 seconds. The switching set and reset voltages are reduced compared to memristors made from single-crystalline MoS2 processed at higher temperatures. For sensing, interdigitated electrode-based gas sensors fabricated on ∼5 nm MoS2 (550 °C, 1.25 hours) show excellent selectivity and sub-ppm sensitivity to NO2 gas, with a notable self-recovery at room temperature without extra energy input.IV. ConclusionsRepeatable, uniform and continuous growth of MoS2 is achieved by CVD approximately at/below BEOL thermal budget limit in a commercial 300 mm reactor. Memristive and gas sensing functionality are achieved from the MoS2 films with an indication of reduced power consumption. This work advances a key enabling technology objective in emerging materials and devices for 3D heterogeneous integration.

Memristor-based biomimetic compound eye for real-time collision detection

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

An Ultra-Compact Design of Plasmonic Memristor with Low Loss and High Extinction Efficiency Based on Enhanced Interaction between Filament and Concentrated Plasmon

We present a numerical design of the plasmonic memristive switching device operated at the telecommunication wavelength of 1.55 μm, which consists of a triangle-shaped metal taper mounted on top of a Si waveguide, with rational doping in the area below the apex of the taper. This device can achieve optimal vertical coupling of light energy from the Si waveguide to the plasmonic region and, at the same time, focus the plasmon into the apex of the metal taper. Moreover, the area with concentrated plasmon is overlapped with that where the memristive switching occurs, due to the formation/removal of the metallic nano-filament. As a result, the highly distinct transmission induced by the switching of the plasmonic memristor can be produced because of the maximized interactions between the filament and the plasmon. Our numerical simulation shows that the device hasa compact size (610 nm), low insertion loss (~1 dB), and high extinction efficiency (4.6 dB/μm). Additionally, we point out that stabilizing the size of the filament is critical to improve the operation repeatability of the plasmonic memristive switching device.

Multilevel switching in Mg-doped HfOx memristor through the mutual-ion effect

HfOx memristor is one of the most promising candidates for nonvolatile memory and neuromorphic computing applications, but for the latter, its gradual conduction modulation and switching linearity still need improvement. In this work, uniform and multilevel resistance switching (RS) of HfOx memristors have been achieved through Mg doping, which is a distinct dopant other than previously used elements like Al and Gd. Moreover, the Mg-doped HfOx memristor exhibits excellent continuous conductance modulation characteristics under pulse voltage. First-principles calculations and conductive mechanism analysis show that Mg can migrate into the conductive filaments (CFs) by adjusting oxygen vacancy migration and defect states in the CFs, resulting in the improved RS performance of HfOx memristors. The reason why Mg doping rather than Ca doping could yield improved RS performances is clarified. Our work shows the unique functionality of Mg dopants in HfOx and provides a feasible way to improve RS performance of transition metal oxide memristors and its guiding principles.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.

Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution‐Processed 2D Semiconductors

AbstractMemristive systems present a low‐power alternative to silicon‐based electronics for neuromorphic and in‐memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large‐area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two‐terminal vertical geometry. Here, the authors report thermally activated memristors fabricated from percolating networks of diverse solution‐processed 2D semiconductors including MoS2, ReS2, WS2, and InSe. The mechanisms underlying threshold switching and negative differential resistance are elucidated by designing large‐area lateral memristors that allow the direct observation of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 103) that are achieved at low fields (≈4 kV cm−1) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution‐processed 2D semiconductors as a platform for neuromorphic architectures.

Epitaxial layered Sb2Te3 thin films for memory and neuromorphic applications

Two-dimensional layered materials have attracted a lot of attention as building block in memristive devices owing to their high downscaling potential, easy stacking due to van der Waals forces and mechanical flexibility. In this study, memristive switching is explored in vertical device structures based on layered Sb2Te3. For this, epitaxial 2D-like Sb2Te3 thin films with thicknesses of ∼20 nm were directly grown on conductive p-type Si (111) substrates by pulsed laser deposition. Analog programmability mimicking neuromorphic operation, stable multilevel retention and endurance performance with a memory window larger than one order of magnitude are achieved by utilizing Ag as electrode metal. However, Cu top electrodes lead to a memristive switching with generally smaller memory window and volatility of programmed states. Devices with both electrode metals offer forming-free operation and self-compliance. Structural and chemical characterization reveal a diffusion of Ag and Cu into the Sb2Te3. It is suggested that charge trapping is involved in the memristive switching mechanism. Overall, this work shows the high potential of thin layered Sb2Te3 for neuromorphic computing and offers a scalable method for integration into the existing Si platform.

Using volatile/non-volatile memristor for emulating the short-and long-term adaptation behavior of the biological neurons

Adaptive response to the timely constant stimulus is the common feature of real neurons. The circuit of the adaptive neuron model consumes less power and requires less data transmission bandwidth compared to the circuit of the non-adaptive neuron model, especially for encoding time-varying signals. Memristor is a good candidate for mimicking the behavior of neurons so that the simple memristor-based circuit can directly emulate many specific behaviors of the neurons with low power and low area consumption. In this work, for the first time, we show that as the nonvolatile switching property of the memristor can be useful for representing long-term adaptation behavior in the memristor-based neuron, the short-term adaptation behavior can also be emulated directly using the same memristor-based circuit due to the volatile switching property of the memristor. Here, short term adaptation is realized using the volatile property of memristor, unlike neuron circuits where adaptation is realized using leakage modulation. As a result, in the memristor-based neuron extra power dissipation can be reduced. Two different types of memristors are used for implementing the proposed circuit of adaptive leaky integrate-and-fire neuron, the volatile/non-volatile memristor and threshold switching memristor are in the charge and discharge path of the capacitor, respectively. Results show that the volatile or non-volatile resistance change of charging memristor upon different input patterns to the neuron circuit determines the type of adaptive behavior of the neuron response, i.e. the neuron may show short-term adaptation or long-term adaptation or does not show an adaptation behavior at all. Comparison with similar works shows that the energy consumption per spiking of the proposed neuron is relatively low, while the circuit is very area-efficient.