arXiv subjects

Yunfei He

Publications and source records attributed to Yunfei He.

8 recordsLinked to original sources

arXiv2026

Compositional gradient engineering for enhanced ferroelectricity in ultrathin AlScN

Ferroelectric AlScN is promising for CMOS-compatible non-volatile memory, but thickness scaling is limited by leakage, premature breakdown, and defect-mediated failure. Here we show that compositional grading within a continuous wurtzite AlN-AlScN lattice mitigates these limitations by distributing structural and polarization discontinuities across the film thickness, reducing defect formation and local field concentration. In a 20 nm graded heterostructure, monotonic Sc incorporation and AlN-rich boundaries produce reversible ferroelectric switching, an as-grown metal-polar state, a 21% higher breakdown field, 10% enhanced remanent polarization, and 40x higher resistivity relative to homogeneous AlScN. Time-domain PUND measurements reveal strongly suppressed post-switching leakage, consistent with reduced defect-assisted and polarization-coupled conduction. This improved dielectric robustness enables ferroelectric functionality in 5 nm graded stacks containing only a 2 nm $\mathrm{Al}_{0.64}\mathrm{Sc}_{0.36}\mathrm{N}$ region, with measurable switching near 1 V. These results establish compositional grading as a defect- and field-management strategy for scalable ultrathin wurtzite ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

Demonstration of highly scaled AlScN ferroelectric diode memory with storage density > 100 Mbit/mm$^2$

Wurtzite nitride ferroelectric materials have emerged as promising candidates for next-generation memory applications due to their exceptional polarization properties and compatibility with conventional semiconductor processing techniques. Here, we demonstrate the first successful areal scaling of Aluminum Scandium Nitride (AlScN) ferroelectric diode (FeDiode) memory down to 40 nm device diameters while maintaining ON/OFF > 60. Using a 20 nm thick Al0.64Sc0.36N ferroelectric layer, we evaluate both metal-insulator-ferroelectric-metal (MIFM) and metal-ferroelectric-metal (MFM) architectures for scaled resistive memory devices. Our scaled devices exhibit an enhanced breakdown-to-coercive field ratio exceeding 2.6 due to increased breakdown field. The MIFM devices demonstrate stable 3-bit non-volatile multistate behavior with clearly distinguishable resistance states and retention exceeding 4*10^4 seconds at 85 C. By achieving more than a million-fold areal scaling with enhanced performance metrics, this work establishes AlScN-based FeDiode memory as a highly promising platform for non-volatile storage with potential for direct integration into CMOS technology.

cond-mat.mes-hall↗

arXiv2025

Write Cycling Endurance Exceeding 1010 in Sub-50 nm Ferroelectric AlScN

Wurtzite ferroelectrics, particularly aluminum scandium nitride (AlScN), have emerged as a promising materials platform for nonvolatile memories, offering high polarization values exceeding 100 uC/cm2. However, their high coercive fields (>3 MV/cm) have limited cycling endurance to ~107 cycles in previous reports. Here, we demonstrate unprecedented control of polarization switching in AlScN, achieving write cycling endurance exceeding 1010 cycles a thousand fold improvement over previous wurtzite ferroelectric benchmarks. Through precise voltage modulation in 45 nm thick Al0.64Sc0.36N capacitors, we show that while complete polarization reversal (2Pr ~ 200 uC/cm2) sustains ~108 cycles, partial switching extends endurance beyond 1010 cycles while maintaining a substantial polarization (>30 uC/cm2 for 2Pr). This exceptional endurance, combined with breakdown fields approaching 10 MV/cm in optimized 10 um diameter devices, represents the highest reported values for any wurtzite ferroelectric. Our findings establish a new paradigm for reliability in nitride ferroelectrics, demonstrating that controlled partial polarization and size scaling enables both high endurance and energy efficient operation.

cond-mat.mtrl-sci↗

arXiv2025

Low-voltage Ferroelectric Field-Effect Transistors with Ultrathin Aluminum Scandium Nitride and 2D channels

The continued evolution of CMOS technology demands materials and architectures that emphasize low power consumption, particularly for computations involving large scale data processing and multivariable optimization. Ferroelectric materials offer promising solutions through enabling dual-purpose memory units capable of performing both storage and logic operations. In this study, we demonstrate ferroelectric field effect transistors (FeFETs) with MoS2 monolayer channels fabricated on ultrathin 5 nm and 10 nm ferroelectric Aluminum Scandium Nitride (AlScN) films. By decreasing the thickness of the ferroelectric film, we achieve significantly reduced gate voltages (<3V) required to switch the conductance of the devices, enabling operation at low voltages compatible with advanced CMOS. We observe a characteristic crossover in hysteresis behavior that varies with film thickness, channel fabrication method, and environmental conditions. Through systematic investigation of multiple parameters including channel fabrication methods, dimensional scaling, and environmental effects, we provide pathways to improve device performance. While our devices demonstrate clear ferroelectric switching behavior, further optimization is required to enhance the ON/OFF ratio at zero gate voltage while continuing to reduce the coercive field of these ultrathin films.

physics.app-ph↗

arXiv2024

Al0.68Sc0.32N/SiC based metal-ferroelectric-semiconductor capacitors operating up to 1000 {\deg}C

Ferroelectric (Fe) materials-based devices show great promise for non-volatile memory applications, yet few demonstrate reliable operation at elevated temperatures. In this work, we demonstrate Ni/Al0.68Sc0.32N/4H-SiC metal-ferroelectric-semiconductor capacitors for high-temperature non-volatile memory applications. Our 30-nm thick ferroelectric Al0.68Sc0.32N film grown on SiC exhibits stable and robust ferroelectric switching up to 1000{\deg}C. The coercive field decreases linearly from -6.4/+11.9 MV cm-1 at room temperature to -3.1/+7.8 MV cm-1 at 800{\deg}C, further reducing to -2.5 MV cm-1 at 1000{\deg}C. At 600{\deg}C, the devices achieve remarkable reliability with ~2000 endurance cycles and over at least 100 hours of retention with negligible polarization loss. At 800{\deg}C, the devices retain data for at least 10,000 seconds and exceed 400 write cycles. Our results further highlight the potential for ferroelectric AlScN thin-films particularly when paired with SiC semiconductor substrates for high-temperature non-volatile memory.

cond-mat.mtrl-sci↗

arXiv2023

Analog Content-Addressable Memory from Complementary FeFETs

To address the increasing computational demands of artificial intelligence (AI) and big data, compute-in-memory (CIM) integrates memory and processing units into the same physical location, reducing the time and energy overhead of the system. Despite advancements in non-volatile memory (NVM) for matrix multiplication, other critical data-intensive operations, like parallel search, have been overlooked. Current parallel search architectures, namely content-addressable memory (CAM), often use binary, which restricts density and functionality. We present an analog CAM (ACAM) cell, built on two complementary ferroelectric field-effect transistors (FeFETs), that performs parallel search in the analog domain with over 40 distinct match windows. We then deploy it to calculate similarity between vectors, a building block in the following two machine learning problems. ACAM outperforms ternary CAM (TCAM) when applied to similarity search for few-shot learning on the Omniglot dataset, yielding projected simulation results with improved inference accuracy by 5%, 3x denser memory architecture, and more than 100x faster speed compared to central processing unit (CPU) and graphics processing unit (GPU) per similarity search on scaled CMOS nodes. We also demonstrate 1-step inference on a kernel regression model by combining non-linear kernel computation and matrix multiplication in ACAM, with simulation estimates indicating 1,000x faster inference than CPU and GPU.

cs.ET↗

arXiv2023

Scalable and Stable Ferroelectric Non-Volatile Memory at > 500 $^\circ$C

Non-volatile memory (NVM) devices that reliably operate at temperatures above 300 $^\circ$C are currently non-existent and remains a critically unmet challenge in the development of high-temperature (T) resilient electronics, necessary for many emerging, complex computing and sensing in harsh environments. Ferroelectric Al$_x$Sc$_{1-x}$N exhibits strong potential for utilization in NVM devices operating at very high temperatures (> 500 $^\circ$C) given its stable and high remnant polarization (PR) above 100 $μ$C/cm$^2$ with demonstrated ferroelectric transition temperature (TC) > 1000 $^\circ$C. Here, we demonstrate an Al$_{0.68}$Sc$_{0.32}$N ferroelectric diode based NVM device that can reliably operate with clear ferroelectric switching up to 600 $^\circ$C with distinguishable On and Off states. The coercive field (EC) from the Pulsed I-V measurements is found to be -5.84 (EC-) and +5.98 (EC+) (+/- 0.1) MV/cm at room temperature (RT) and found to decrease with increasing temperature up to 600 $^\circ$C. The devices exhibit high remnant polarizations (> 100 $μ$C/cm$^2$) which are stable at high temperatures. At 500 $^\circ$C, our devices show 1 million read cycles and stable On-Off ratio above 1 for > 6 hours. Finally, the operating voltages of our AlScN ferrodiodes are < 15 V at 600 $^\circ$C which is well matched and compatible with Silicon Carbide (SiC) based high temperature logic technology, thereby making our demonstration a major step towards commercialization of NVM integrated high-T computers.

physics.app-ph↗

arXiv2022

Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at device level to enable novel compute-in-memory (CIM) operations. A key challenge in construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search and neural network operations on sub-50nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, non-volatility and non-linearity of FeDs, search operations are demonstrated with a cell footprint < 0.12 um2 when projected onto 45-nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms.

physics.app-ph↗