
Physical Review Research, Journal Year: 2025, Volume and Issue: 7(2)
Published: April 3, 2025
Oxygen-deficient
titanium
dioxide
(
Language: Английский
Physical Review Research, Journal Year: 2025, Volume and Issue: 7(2)
Published: April 3, 2025
Oxygen-deficient
titanium
dioxide
(
Language: Английский
ACS Nano, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 12, 2025
Ferroelectric tunnel junctions (FTJs) harness the combination of ferroelectricity and quantum tunneling thus herald opportunities in next-generation nonvolatile memory technologies. Recent advancements fabrication ultrathin heterostructures have enabled integration ferroelectrics with various functional materials, forming hybrid tunneling-diode junctions. These benefit from modulation layer/ferroelectric interface through ferroelectric polarization, enabling further modalities capabilities addition to electroresistance. This Perspective aims provide in-depth insight into physical phenomena several typical junctions, ranging ferroelectric/dielectric, ferroelectric/multiferroic, ferroelectric/superconducting ferroelectric/2D finally their expansion realm resonant diodes (FeRTDs). latter aspect, i.e., tunneling, offers an approach exploiting behavior heterostructures. We discuss examples that successfully shown room-temperature control parameters such as peak, current ratio at negative differential resistance. conclude by summarizing challenges highlighting for future development FTJs, a special emphasis on possible type FeRTD device. The prospects enhanced performance expanded functionality ignite tremendous excitement FTJs FeRTDs nanoelectronics.
Language: Английский
Citations
0ACS Applied Electronic Materials, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 26, 2025
Language: Английский
Citations
0Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown
Published: March 14, 2025
Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging realize in practice. The learning‐from‐mistakes algorithm, inspired by brain's simple learning rules inhibition pruning, is one few brain‐like training methods. This algorithm implemented neuromorphic memristive hardware through a codesign process that evaluates essential trade‐offs. While effectively trains small networks as binary classifiers perceptrons, performance declines significantly with increasing network size unless tailored algorithm. work investigates trade‐offs between depth, controllability, capacity—the number learnable patterns—in hardware. highlights importance topology governing equations, providing theoretical tools evaluate device's computational capacity based on its measurements circuit structure. findings show breaking neural symmetry enhances both controllability capacity. Additionally, pruning circuit, algorithms all‐memristive circuits can utilize stochastic resources create local contrasts weights. Through combined experimental simulation efforts, parameters are identified enable exhibit emergent intelligence from rules, advancing computing.
Language: Английский
Citations
0Surfaces and Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 106315 - 106315
Published: March 1, 2025
Language: Английский
Citations
0Physical Review Research, Journal Year: 2025, Volume and Issue: 7(2)
Published: April 3, 2025
Oxygen-deficient
titanium
dioxide
(
Language: Английский
Citations
0