Search Results (10)

The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals. Full article

Computing systems are becoming more and more power-constrained due to unconventional computing requirements like computing on the edge, in-sensor, or simply an insufficient battery. Emerging Non-Volatile Memories are explored to build low-power computing circuits, and adders are one among them. In this work, we propose a low-power adder using a Ferroelectric Tunnel Junction (FTJ). FTJs are two-terminal devices where the data is stored in the polarization state of the device. An FTJ-based majority gate is proposed, which uses a current-mode sensing technique to evaluate the majority of the inputs. By conditionally selecting between the majority and its complement, an XOR operation is implemented, thereby achieving full-adder functionality. Since FTJ-based majority operation is slow, a ternary adder architecture is used to compensate for the speed loss. The ternary adder proposed by us has two stages of full adder and requires O(1) time for n-bit addition. The proposed adder is verified using a simulation in CMOS 130 nm technology. A 32-bit addition can be achieved in 100 μs and consumes 0.78 pJ, which is very power efficient (7.8 nW). The proposed adder can be used in applications where power consumption is crucial, and speed is not a strict requirement. Full article

Recently, considerable attention has been paid to the development of advanced technologies such as artificial intelligence (AI) and big data, and high-density, high-speed storage devices are being extensively studied to realize the technology. Ferroelectrics are promising non-volatile memory materials because of their ability to maintain polarization, even when an external electric field is removed. Recently, it has been reported that HfO₂ thin films compatible with complementary metal–oxide–semiconductor (CMOS) processes exhibit ferroelectricity even at a thickness of less than 10 nm. Among the ferroelectric-based memories, ferroelectric tunnel junctions are attracting attention as ideal devices for improving integration and miniaturization due to the advantages of a simple metal–ferroelectric–metal two-terminal structure and low ultra-low power driving through tunneling. The FTJs are driven by adjusting the tunneling electrical resistance through partial polarization switching. Theoretically and experimentally, a large memory window in a broad coercive field and/or read voltage is required to induce sophisticated partial-polarization switching. Notably, antiferroelectrics (like) have different switching properties than ferroelectrics, which are generally applied to ferroelectric tunnel junctions. The memory features of ferroelectric tunnel junctions are expected to be improved through a broad coercive field when the switching characteristics of the ferroelectric and antiferroelectric (like) are utilized concurrently. In this study, the implementation of multiresistance states was improved by driving the ferroelectric and antiferroelectric (like) devices in parallel. Additionally, by modulating the area ratio of ferroelectric and antiferroelectric (like), the memory window size was increased, and controllability was enhanced by increasing the switchable voltage region. In conclusion, we suggest that ferroelectric and antiferroelectric (like) parallel structures may overcome the limitations of the multiresistance state implementation of existing ferroelectrics. Full article

Owing to the 4th Industrial Revolution, the amount of unstructured data, such as voice and video data, is rapidly increasing. Brain-inspired neuromorphic computing is a new computing method that can efficiently and parallelly process rapidly increasing data. Among artificial neural networks that mimic the structure of the brain, the spiking neural network (SNN) is a network that imitates the information-processing method of biological neural networks. Recently, memristors have attracted attention as synaptic devices for neuromorphic computing systems. Among them, the ferroelectric doped-HfO₂-based ferroelectric tunnel junction (FTJ) is considered as a strong candidate for synaptic devices due to its advantages, such as complementary metal–oxide–semiconductor device/process compatibility, a simple two-terminal structure, and low power consumption. However, research on the spiking operations of FTJ devices for SNN applications is lacking. In this study, the implementation of long-term depression and potentiation as the spike timing-dependent plasticity (STDP) rule in the FTJ device was successful. Based on the measured data, a CrossSim simulator was used to simulate the classification of handwriting images. With a high accuracy of 95.79% for the Mixed National Institute of Standards and Technology (MNIST) dataset, the simulation results demonstrate that our device is capable of differentiating between handwritten images. This suggests that our FTJ device can be used as a synaptic device for implementing an SNN. Full article

A transient chaotic neural network (TCNN) is particularly useful for solving combinatorial optimization problems, and its hardware implementation based on memristors has attracted great attention recently. Although previously used filamentary memristors could provide the desired nonlinearity for implementing the annealing function of a TCNN, the controllability of filamentary switching still remains relatively poor, thus limiting the performance of a memristor-based TCNN. Here, we propose to use ferroelectric memristor to implement the annealing function of a TCNN. In the ferroelectric memristor, the conductance can be tuned by switching the lattice non-centrosymmetry-induced polarization, which is a nonlinear switching mechanism with high controllability. We first establish a ferroelectric memristor model based on a ferroelectric tunnel junction (FTJ), which exhibits the polarization-modulated tunnel conductance and the nucleation-limited-switching (NLS) behavior. Then, the conductance of the ferroelectric memristor is used as the self-feedback connection weight that can be dynamically adjusted. Based on this, a ferroelectric memristor-based transient chaotic neural network (FM-TCNN) is further constructed and applied to solve the traveling salesman problem (TSP). In 1000 runs for 10-city TSP, the FM-TCNN achieves a shorter average path distance, a 32.8% faster convergence speed, and a 2.44% higher global optimal rate than the TCNN. Full article

CuInP2S6 (CIPS) is a novel two-dimensional (2D) van der Waals (vdW) ferroelectric layered material with a Curie temperature of T_C~315 K, making it promising for great potential applications in electronic and photoelectric devices. Herein, the ferroelectric and electric properties of CIPS at different thicknesses are carefully evaluated by scanning probe microscopy techniques. Some defects in some local regions due to Cu deficiency lead to a CuInP2S6–In_4/3P₂S₆ (CIPS–IPS) paraelectric phase coexisting with the CIPS ferroelectric phase. An electrochemical strain microscopy (ESM) study reveals that the relaxation times corresponding to the Cu ions and the IPS ionospheres are not the same, with a significant difference in their response to DC voltage, related to the rectification effect of the ferroelectric tunnel junction (FTJ). The electric properties of the FTJ indicate Cu⁺ ion migration and propose that the current flow and device performance are dynamically controlled by an interfacial Schottky barrier. The addition of the ferroelectricity of CIPS opens up applications in memories and sensors, actuators, and even spin-orbit devices based on 2D vdW heterostructures. Full article

The success of a ferroelectric tunnel junction (FTJ) depends on the asymmetry of electron tunneling as given by the tunneling electroresistance (TER) effect. This characteristic is mainly assessed considering three transport mechanisms: direct tunneling, thermionic emission, and Fowler-Nordheim tunneling. Here, by analyzing the effect of temperature on TER, we show that taking into account only these mechanisms may not be enough in order to fully characterize the performance of FTJ devices. We approach the electron tunneling in FTJ with the non-equilibrium Green function (NEGF) method, which is able to overcome the limitations affecting the three mechanisms mentioned above. We bring evidence that the performance of FTJs is also affected by temperature–in a non-trivial way–via resonance (Gamow-Siegert) states, which are present in the electron transmission probability and are usually situated above the barrier. Although the NEGF technique does not provide direct access to the wavefunctions, we show that, for single-band transport, one can find the wavefunction at any given energy and in particular at resonant energies in the system. Full article

Ferroelectric tunnel junctions (FTJs) have attracted attention as devices for advanced memory applications owing to their high operating speed, low operating energy, and excellent scalability. In particular, hafnia ferroelectric materials are very promising because of their high remanent polarization (below 10 nm) and high compatibility with complementary metal-oxide-semiconductor (CMOS) processes. In this study, a Si-doped HfO₂-based FTJ device with a metal-ferroelectric-insulator-semiconductor (MFIS) structure was proposed to maximize the tunneling electro-resistance (TER) effect. The potential barrier modulation effect under applied varying voltage was analyzed, and the possibility of its application as a non-volatile memory device was presented through stability assessments such as endurance and retention tests. Full article

The discovery of ferroelectricity in HfO₂ thin film, which is compatible with the CMOS process, has revived interest in ferroelectric memory devices. HfO₂ has been found to exhibit high ferroelectricity at a few nanometers thickness, and studies have rapidly progressed in the past decade. Ferroelectricity can be induced in HfO₂ by various deposition methods and heat treatment processes. By combining ferroelectric materials with field-effect transistors, devices that combine logic and memory functions can be implemented. Ferroelectric HfO₂-based devices show high potential, but there are some challenges to overcome in endurance and characterization. In this paper, we discuss the fabrication and characteristics of ferroelectric HfO₂ film and various applications, including negative capacitance (NC)), Ferroelectric random-access memory (FeRAM), Ferroelectric tunnel junction (FTJ), and Ferroelectric Field-effect Transistor (FeFET). Full article

Nanomaterials science is becoming the foundation stone of high-frequency applications. The downscaling of electronic devices and components allows shrinking chip’s dimensions at a more-than-Moore rate. Many theoretical limits and manufacturing constraints are yet to be taken into account. A promising path towards nanoelectronics is represented by atomic-scale materials. In this manuscript, we offer a perspective on a specific class of devices, namely switches designed and fabricated using two-dimensional or nanoscale materials, like graphene, molybdenum disulphide, hexagonal boron nitride and ultra-thin oxides for high-frequency applications. An overview is provided about three main types of microwave and millimeter-wave switch: filament memristors, nano-ionic memristors and ferroelectric junctions. The physical principles that govern each switch are presented, together with advantages and disadvantages. In the last part we focus on zirconium-doped hafnium oxide ferroelectrics (HfZrO) tunneling junctions (FTJ), which are likely to boost the research in the domain of atomic-scale materials applied in engineering sciences. Thanks to their Complementary Metal-Oxide Semiconductor (CMOS) compatibility and low-voltage tunability (among other unique physical properties), HfZrO compounds have the potential for large-scale applicability. As a practical case of study, we present a 10 GHz transceiver in which the switches are FTJs, which guarantee excellent isolation and ultra-fast switching time. Full article