98%
921
2 minutes
20
Multilevel storage and low-voltage operation position ferroelectric transistors as promising candidates for next-generation nonvolatile memory. Among them, gate-injection-type ferroelectric transistors offer improved vertical scalability and power efficiency for three-dimensional (3D) NAND flash. However, their intricate interplay between polarization switching and charge trapping complicates systematic understanding of degradation mechanisms, limiting strategies to improve reliability and stability. Here, gate stack engineering incorporating middle interlayers within HfZrO matrix is presented to modulate polarization dynamics, strengthening the coupling of dual mechanisms and overcoming long-standing reliability and stability bottlenecks in ferroelectric NAND operation. This approach achieves a memory window up to 11 V, an operating voltage below 18 V, triple-level-cell retention beyond 10 years, disturbance immunity, and 54% reduced threshold voltage variability. A 20% reduction in program voltage compared to conventional NAND enables aggressive vertical scaling, leading to 25% higher bit-density. Furthermore, analytical modeling provides insights into gate stack optimization. These findings establish ferroelectric NAND as a scalable, energy-efficient solution for next-generation storage.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/advs.202510155 | DOI Listing |
Adv Sci (Weinh)
August 2025
Department of Electrical engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
Multilevel storage and low-voltage operation position ferroelectric transistors as promising candidates for next-generation nonvolatile memory. Among them, gate-injection-type ferroelectric transistors offer improved vertical scalability and power efficiency for three-dimensional (3D) NAND flash. However, their intricate interplay between polarization switching and charge trapping complicates systematic understanding of degradation mechanisms, limiting strategies to improve reliability and stability.
View Article and Find Full Text PDFNat Commun
July 2025
Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
The rapid advancement of artificial intelligence has enabled breakthroughs in diverse fields, including autonomous systems and medical diagnostics. However, conventional deterministic neural networks struggle to capture uncertainty, limiting their reliability when handling real-world data, which are often noisy, imbalanced, or scarce. Bayesian neural networks address this limitation by representing weights as probabilistic distributions, allowing for natural uncertainty quantification and improved robustness.
View Article and Find Full Text PDFACS Appl Mater Interfaces
June 2025
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.
Ferroelectric materials, known for their nonvolatile and reversible polarization states, are emerging as promising candidates for innovative computing paradigms such as neuromorphic computing and logic-in-memory (LiM) architectures. Their polarization dynamics in response to external stimuli closely emulates biological synapses, a feature crucial for learning and adaptation in neural networks. Achieving multiple intermediate states between fully polarized states is critical for energy-efficient computation.
View Article and Find Full Text PDFAdv Mater
March 2025
State Key Laboratory of High-Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, 200050, China.
Differentiating photoelectric response in a single material with a simple approach is desirable for all-in-one optoelectronic logical devices. In ferroelectric materials, significantly distinct photoelectric features should be observed if they are in diverse polarization states, unveiling a possible pathway to realize multifunctional optoelectronic logic gates through ferroelectric polarization design. In this study, the Ti self-doping strategy is first applied to 0.
View Article and Find Full Text PDFNat Commun
January 2025
Hangzhou Institute of Technology, Xidian University, Hangzhou, 311231, China.
Edge detection is one of the most essential research hotspots in computer vision and has a wide variety of applications, such as image segmentation, target detection, and other high-level image processing technologies. However, efficient edge detection is difficult in a resource-constrained environment, especially edge-computing hardware. Here, we report a low-power edge detection hardware system based on HfO-based ferroelectric field-effect transistor, which is one of the most potential non-volatile memories for energy-efficient computing.
View Article and Find Full Text PDF