Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network.

Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.

Ferroelectric Hafnia-Based M3D FeTFTs Annealed at Extremely Low Temperatures and TCAM Cells for Computing-in-Memory Applications.

In order to overcome the bottleneck between the central processor unit and memory as well as the issue of energy consumption, computing-in-memory (CIM) is becoming more popular as an alternative to the traditional von Neumann structure. However, as artificial intelligence advances, the networks require CIM devices to store billions of parameters in order to handle huge data traffic demands. Monolithic three-dimensional (M3D) stacked ferroelectric thin-film transistors (FeTFTs) are one of the promising techniques for realizing high-density CIM devices that can store billions of parameters. In particular, oxide channel-based FeTFTs are well suited for these applications due to low-temperature processes, nonvolatility, and 3D integration capability. Nevertheless, the M3D-integrated CIM devices including hafnia ferroelectric films need the high-temperature annealing process to crystallize the ferroelectric layer, making M3D integration difficult. When the FeTFTs are fabricated with an M3D structure, the high-temperature process causes thermal issues in the underlying devices. Here, we present the focused microwave annealed (FMA) oxide FeTFTs with M3D integration at a low temperature of 250 °C. We confirmed that the FeTFTs with metal-ferroelectric-metal-insulator-semiconductor structure exhibited a large memory window of 3.2 V, good endurance over 106 cycles, and a long retention time of 105 s. To understand the different electrical characteristics of FeTFTs in the top and bottom layers, we experimentally analyzed the density of the state of the oxide channel and ferroelectric properties of the ferroelectric gate insulator by using multifrequency capacitance-voltage measurement and nucleation-limited-switching model analysis, respectively. With our approach, we demonstrate for the first time a vertical stacked FeTFTs-based ternary-content-addressable memory (TCAM) cell for CIM application. We believe that the proposed M3D-stacked TCAM cells composed of FeTFTs can be used in high-density memory, energy-efficient memory, and CIM technology.

Flexible Ferroelectric Hafnia-Based Synaptic Transistor by Focused-Microwave Annealing.

Hafnia-based ferroelectric memory devices with excellent ferroelectricity, low power consumption, and fast operation speed have attracted considerable interest with the ever-growing desire for nonvolatile memory in flexible electronics. However, hafnia films are required to perform a high temperature (>500 °C) annealing process for crystallization into the ferroelectric orthorhombic phase. It can hinder the integration of hafnia ferroelectric films on flexible substrates including plastic and polymer, which are not endurable at high temperatures above 300 °C. Here, we propose the extremely low-temperature (∼250 °C) process for crystallization of Hf0.5Zr0.5O2 (HZO) thin films by applying a focused-microwave induced annealing method. HZO thin films on a flexible mica substrate exhibits robust remnant polarization (2Pr ∼ 50 µC/cm2), which is negligibly changed under bending tests. In addition, the electrical characteristics of a HZO capacitor on the mica substrate were evaluated, and ferroelectric thin film transistors (Fe-TFTs), using a HZO gate insulator, were fabricated on mica substrates for flexible synapse applications. Symmetric potentiation and depression characteristics are successfully demonstrated in the Fe-TFT memory devices, and the synaptic devices result in high recognition accuracy of 91.44%. The low-temperature annealing method used in this work are promising for forming hafnia-based Fe-TFT memory devices as a building block on a flexible platform.

Design of exceptionally strong and conductive Cu alloys beyond the conventional speculation via the interfacial energy-controlled dispersion of γ-Al2O3 nanoparticles.

The development of Cu-based alloys with high-mechanical properties (strength, ductility) and electrical conductivity plays a key role over a wide range of industrial applications. Successful design of the materials, however, has been rare due to the improvement of mutually exclusive properties as conventionally speculated. In this paper, we demonstrate that these contradictory material properties can be improved simultaneously if the interfacial energies of heterogeneous interfaces are carefully controlled. We uniformly disperse γ-Al2O3 nanoparticles over Cu matrix, and then we controlled atomic level morphology of the interface γ-Al2O3//Cu by adding Ti solutes. It is shown that the Ti dramatically drives the interfacial phase transformation from very irregular to homogeneous spherical morphologies resulting in substantial enhancement of the mechanical property of Cu matrix. Furthermore, the Ti removes impurities (O and Al) in the Cu matrix by forming oxides leading to recovery of the electrical conductivity of pure Cu. We validate experimental results using TEM and EDX combined with first-principles density functional theory (DFT) calculations, which all consistently poise that our materials are suitable for industrial applications.