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.