Controlling Cu Migration on Resistive Switching, Artificial Synapse, and Glucose/Saliva Detection by Using an Optimized AlO x Interfacial Layer in a-CO x -Based Conductive Bridge Random Access Memory

Heterogeneous Integration of Memristive and Piezoresistive MDMO-PPV-Based Copolymers in Nociceptive Transmission with Fast and Slow Pain for an Artificial Pain-Perceptual System

Nociceptive pain perception is a remarkable capability of organisms to be aware of environmental changes and avoid injury, which can be accomplished by specialized pain receptors known as nociceptors with 4 vital properties including threshold, no adaptation, relaxation, and sensitization. Bioinspired systems designed using artificial devices are investigated to imitate the efficacy and functionality of nociceptive transmission. Here, an artificial pain-perceptual system (APPS) with a homogeneous material and heterogeneous integration is proposed to emulate the behavior of fast and slow pain in nociceptive transmission. Retention-differentiated poly[2-methoxy-5-(3,7-dimethyoctyoxyl)-1,4-phenylenevinylene] (MDMO-PPV) memristors with film thicknesses of 160 and 80 nm are manufactured and adopted as A-δ and C nerve fibers of nociceptor conduits, respectively. Additionally, a nociceptor mimic, the ruthenium nanoparticles (Ru-NPs)-doped MDMO-PPV piezoresistive pressure sensor, is fabricated with a noxiously stimulated threshold of 150 kPa. Under the application of pricking and dull noxious stimuli, the current flows predominantly through the memristor to mimic the behavior of fast and slow pain, respectively, in nociceptive transmission with postsynaptic potentiation properties, which is analogous to biological pain perception. The proposed APPS can provide potential advancements in establishing the nervous system, thus enabling the successful development of next-generation neurorobotics, neuroprosthetics, and precision medicine.

Understanding the Resistive Switching Behaviors of Top Electrode (Au, Cu, and Al)-Dependent TiO2-Based Memristive Devices

Memristor-based neuromorphic computing is promising toward their potential application of handling complex parallel tasks in the period of big data. To implement brain-inspired applications of spiking neural networks, new physical architecture designs are needed. Here, a serial memristive structure (SMS) consisting of memristive devices with different top electrodes is proposed. Top electrodes Au, Cu, and Al are selected for nitrogen-doped TiO2 nanorod array-based memristive devices. The typical I-V cycles, retention, on/off ratio, and variations of cycle to cycle of top electrode-dependent memristive devices have been studied. Devices with Cu and Al electrodes exhibit a retention of over 104 s. And the resistance states of the device with the Al top electrode are reliable. Furthermore, the conductive mechanism underlining the I-V curves is discussed in detail. The interface-type mechanism and block conductance mechanism are illustrated, which are related to electron migration and ion/anion migration, respectively. Finally, the SMS has been constructed using memristive devices with Al and Cu top electrodes, which can mimic the spiking pulse-dependent plasticity of a synapse and a neuron body. The SMS provides a new approach to implement a fundamental physical unit for neuromorphic computing.

Bi2O2Se-based CBRAM integrated artificial synapse

Integrating two-dimensional (2D) semiconducting materials into memristor structures has paved the way for emerging 2D materials to be employed in a vast field of memory applications. Bismuth oxyselenide (Bi2O2Se), a 2D material with high electron mobility, has attracted significant research interest owing to its great potential in various fields of advanced applications. Here, we explore the out-of-plane intrinsic switching behavior of few-layered Bi2O2Se via a cross point device for application in conductive bridge random access memory (CBRAM) and artificial synapses for neuromorphic computing. Via state-of-the-art methods, CVD-grown Bi2O2Se nanoplate is applied as a switching material (SM) in an Al/Cu/Bi2O2Se/Pd CBRAM structure. The device exhibits ∼90 consecutive DC cycles with a tight distribution of the SET/RESET voltages under a compliance current (CC) of 1 mA, a retention of over 10 ks, and multilevel switching characteristics showing four distinct states at Vread values of 0.1, 0.2, 0.25, and 0.3 V. Moreover, an artificial synapse is realized with potentiation and depression by modulating the conductance. The switching mechanism is explained via Cu migration through Bi2O2Se based on HRTEM analysis. The present structure shows potential for future integrated memory applications.

Interfacing Biology and Electronics with Memristive Materials

Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

Organismic Memristive Structures With Variable Functionality for Neuroelectronics

In this paper, we report an approach to design nanolayered memristive compositions based on TiO2/Al2O3 bilayer structures with analog non-volatile and volatile tuning of the resistance. The structure of the TiO2 layer drives the physical mechanism underlying the non-volatile resistance switching, which can be changed from electronic to ionic, enabling the synaptic behavior emulation. The presence of the anatase phase in the amorphous TiO2 layer induces the resistive switching mechanism due to electronic processes. In this case, the switching of the resistance within the range of seven orders of magnitude is experimentally observed. In the bilayer with amorphous titanium dioxide, the participation of ionic processes in the switching mechanism results in narrowing the tuning range down to 2–3 orders of magnitude and increasing the operating voltages. In this way, a combination of TiO2/Al2O3 bilayers with inert electrodes enables synaptic behavior emulation, while active electrodes induce the neuronal behavior caused by cation density variation in the active Al2O3 layer of the structure. We consider that the proposed approach could help to explore the memristive capabilities of nanolayered compositions in a more functional way, enabling implementation of artificial neural network algorithms at the material level and simplifying neuromorphic layouts, while maintaining all benefits of neuromorphic architectures.

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous-defective bottom layer (NP-DBL) structure based on amorphous TiO₂ is demonstrated (Ag/a-TiO₂/a-TiO_x/p-Si). The stoichiometries of DBL and the pore size can be manipulated to achieve the analog conductance updates and multilevel conductance by 300 states with 1.3% variation, and 10 levels, respectively. Compared with nonporous TiO₂ CBRAM, endurance, retention, and uniformity can be improved by 10⁶ pulses, 28 days at 85°C, and 6.7 times, respectively. These results suggest even amorphous-based systems, elaborately tuned structural variables, can help design more reliable CBRAMs.

Contact Engineering Approach to Improve the Linearity of Multilevel Memristive Devices

Physical mechanisms underlying the multilevel resistive tuning over seven orders of magnitude in structures based on TiO₂/Al₂O₃ bilayers, sandwiched between platinum electrodes, are responsible for the nonlinear dependence of the conductivity of intermediate resistance states on the writing voltage. To improve the linearity of the electric-field resistance tuning, we apply a contact engineering approach. For this purpose, platinum top electrodes were replaced with aluminum and copper ones to induce the oxygen-related electrochemical reactions at the interface with the Al₂O₃ switching layer of the structures. Based on experimental results, it was found that electrode material substitution provokes modification of the physical mechanism behind the resistive switching in TiO₂/Al₂O₃ bilayers. In the case of aluminum electrodes, a memory window has been narrowed down to three orders of magnitude, while the linearity of resistance tuning was improved. For copper electrodes, a combination of effects related to metal ion diffusion with oxygen vacancies driven resistive switching was responsible for a rapid relaxation of intermediate resistance states in TiO₂/Al₂O₃ bilayers.

Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems

In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.

Switching Characteristics and Mechanism Using Al2O3 Interfacial Layer in Al/Cu/GdOx/Al2O3/TiN Memristor

Resistive switching characteristics by using the Al2O3 interfacial layer in an Al/Cu/GdOx/Al2O3/TiN memristor have been enhanced as compared to the Al/Cu/GdOx/TiN structure owing to the insertion of Al2O3 layer for the first time. Polycrystalline grain, chemical composition, and surface roughness of defective GdOx film have been investigated by transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and atomic force microscopy (AFM). For bipolar resistive switching (BRS) characteristics, the conduction mechanism of high resistance state (HRS) is a space-charge limited current for the Al/Cu/GdOx/TiN device while the Al/Cu/GdOx/Al2O3/TiN device shows Schottky emission. However, both devices show Ohmic at a low resistance state (LRS). After the device has been SET, the Cu filament evidences by both TEM and elemental mapping. Oxygen-rich at the Cu/GdOx interface and Al2O3 layer are confirmed by energy dispersive X-ray spectroscopy (EDS) line profile. The Al/Cu/GdOx/Al2O3/TiN memristor has lower RESET current, higher speed operation of 100 ns, long read pulse endurance of >109 cycles, good data retention, and the memristor with a large resistance ratio of >105 is operated at a low current of 1.5 µA. The complementary resistive switching (CRS) characteristics of the Al/Cu/GdOx/Al2O3/TiN memristor show also a low current operation as compared to the Al/Cu/GdOx/TiN device (300 µA vs. 3.1 mA). The transport mechanism is the Cu ion migration and it shows Ohmic at low field and hopping at high field regions. A larger hopping distance of 1.82 nm at the Cu/GdOx interface is obtained as compared to a hopping distance of 1.14 nm in the Al2O3 layer owing to a larger Cu filament length at the Cu/GdOx interface than the Al2O3 layer. Similarly, the CRS mechanism is explained by using the schematic model. The CRS characteristics show a stable state with long endurance of >1000 cycles at a pulse width of 1 µs owing to the insertion of Al2O3 interfacial layer in the Al/Cu/GdOx/Al2O3/TiN structure.