Deep random forest with ferroelectric analog content addressable memory.

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

Ferroelectric Content-Addressable Memory Cells with IGZO Channel: Impact of Retention Degradation on the Multibit Operation.

Indium gallium zinc oxide (IGZO)-based ferroelectric thin-film transistors (FeTFTs) are being vigorously investigated for being deployed in computing-in-memory (CIM) applications. Content-addressable memories (CAMs) are the quintessential example of CIM, which conduct a parallel search over a queue or stack to obtain the matched entries for a given input data. CAM cells offer the ability for massively parallel searches in a single clock cycle throughout an entire CAM array for the input query, thereby enabling pattern matching and searching functionality. Therefore, CAM cells are used extensively for pattern matching or search operations in data-centric computing. This paper investigates the impact of retention degradation on IGZO-based FeTFT on the multibit operation in content CAM cell applications. We propose a scalable multibit 1FeTFT-1T-based CAM cell composed of only one FeTFT and one transistor, thus significantly improving the density and energy efficiency compared with conventional complementary metal-oxide-semiconductor (CMOS)-based CAM. We successfully demonstrate the operations of our proposed CAM with storage and search by exploiting the multilevel states of the experimentally calibrated IGZO-based FeTFT devices. We also investigate the impact of retention degradation on the search operation. Our proposed IGZO-based 3-bit and 2-bit CAM cell shows 104 s and 106 s retention, respectively. The single-bit CAM cell shows lifelong (10 years) retention.

Synergistic Approach of Interfacial Layer Engineering and READ-Voltage Optimization in HfO2-Based FeFETs for In-Memory-Computing Applications.

This article reports an improvement in the performance of the hafnium oxide-based (HfO2) ferroelectric field-effect transistors (FeFET) achieved by a synergistic approach of interfacial layer (IL) engineering and READ-voltage optimization. FeFET devices with silicon dioxide (SiO2) and silicon oxynitride (SiON) as IL were fabricated and characterized. Although the FeFETs with SiO2 interfaces demonstrated better low-frequency characteristics compared to the FeFETs with SiON interfaces, the latter demonstrated better WRITE endurance and retention. Finally, the neuromorphic simulation was conducted to evaluate the performance of FeFETs with SiO2 and SiON IL as synaptic devices. We observed that the WRITE endurance in both types of FeFETs was insufficient to carry out online neural network training. Therefore, we consider an inference-only operation with offline neural network training. The system-level simulation reveals that the impact of systematic degradation via retention degradation is much more significant for inference-only operation than low-frequency noise. The neural network with FeFETs based on SiON IL in the synaptic core shows 96% accuracy for the inference operation on the handwritten digit from the Modified National Institute of Standards and Technology (MNIST) data set in the presence of flicker noise and retention degradation, which is only a 2.5% deviation from the software baseline.

Structural and Electrical Response of Emerging Memories Exposed to Heavy Ion Radiation.

Hafnium oxide- and GeSbTe-based functional layers are promising candidates in material systems for emerging memory technologies. They are also discussed as contenders for radiation-harsh environment applications. Testing the resilience against ion radiation is of high importance to identify materials that are feasible for future applications of emerging memory technologies like oxide-based, ferroelectric, and phase-change random-access memory. Induced changes of the crystalline and microscopic structure have to be considered as they are directly related to the memory states and failure mechanisms of the emerging memory technologies. Therefore, we present heavy ion irradiation-induced effects in emerging memories based on different memory materials, in particular, HfO2-, HfZrO2-, as well as GeSbTe-based thin films. This study reveals that the initial crystallinity, composition, and microstructure of the memory materials have a fundamental influence on their interaction with Au swift heavy ions. With this, we provide a test protocol for irradiation experiments of hafnium oxide- and GeSbTe-based emerging memories, combining structural investigations by X-ray diffraction on a macroscopic, scanning transmission electron microscopy on a microscopic scale, and electrical characterization of real devices. Such fundamental studies can be also of importance for future applications, considering the transition of digital to analog memories with a multitude of resistance states.

Electric field-induced crystallization of ferroelectric hafnium zirconium oxide.

Ferroelectricity in crystalline hafnium oxide thin films is strongly investigated for the application in non-volatile memories, sensors and other applications. Especially for back-end-of-line (BEoL) integration the decrease of crystallization temperature is of major importance. However, an alternative method for inducing ferroelectricity in amorphous or semi-crystalline hafnium zirconium oxide films is presented here, using the newly discovered effect of electric field-induced crystallization in hafnium oxide films. When applying this method, an outstanding remanent polarization value of 2P[Formula: see text] = 47 [Formula: see text]C/cm[Formula: see text] is achieved for a 5 nm thin film. Besides the influence of Zr content on the film crystallinity, the reliability of films crystallized with this effect is explored, highlighting the controlled crystallization, excellent endurance and long-term retention.

Structural and Electrical Comparison of Si and Zr Doped Hafnium Oxide Thin Films and Integrated FeFETs Utilizing Transmission Kikuchi Diffraction.

The microstructure of ferroelectric hafnium oxide plays a vital role for its application, e.g., non-volatile memories. In this study, transmission Kikuchi diffraction and scanning transmission electron microscopy STEM techniques are used to compare the crystallographic phase and orientation of Si and Zr doped HfO2 thin films as well as integrated in a 22 nm fully-depleted silicon-on-insulator (FDSOI) ferroelectric field effect transistor (FeFET). Both HfO2 films showed a predominately orthorhombic phase in accordance with electrical measurements and X-ray diffraction XRD data. Furthermore, a stronger texture is found for the microstructure of the Si doped HfO2 (HSO) thin film, which is attributed to stress conditions inside the film stack during crystallization. For the HSO thin film fabricated in a metal-oxide-semiconductor (MOS) like structure, a different microstructure, with no apparent texture as well as a different fraction of orthorhombic phase is observed. The 22 nm FDSOI FeFET showed an orthorhombic phase for the HSO layer, as well as an out-of-plane texture of the [111]-axis, which is preferable for the application as non-volatile memory.

Early patellofemoral osteoarthritis caused by an osteochondral defect after retrograde solid nailing of the femur in sheep.

BACKGROUND: Retrograde femoral nailing (RFN) is an increasingly used technique for internal fixation of femoral fractures. Geometrically and empirically, the nail entry zone is close to the center of the femoral groove, causing concern about the development of patellofemoral osteoarthritis. METHODS: We studied the effect of opening the distal femur through the femoral groove on the development of osteoarthritis in sheep after retrograde reamed insertion of a solid titanium nail into the femoral canal. Knees were radiographically and macroscopically studied for the presence of osteophytes and signs of cartilage degeneration. Controls underwent the same procedure without opening the femoral groove. RESULTS: The study group showed time-dependent macroscopic and radiographic signs of osteoarthritis with predominant involvement of the patellofemoral joint. CONCLUSION: RFN can cause patellofemoral osteoarthritis. Care should be exercised to use RFN in isolated supracondylar or shaft fractures of the femur in healthy young adults.