Sub‐Microsecond Polarization Switching in (Al,Sc)N Ferroelectric Capacitors Grown on Complementary Metal–Oxide–Semiconductor‐Compatible Aluminum Electrodes

Wang, Dixiong; Musavigharavi, Pariasadat; Zheng, Jeffrey; Esteves, Giovanni; Liu, Xiwen; Fiagbenu, Merrilyn Mercy Adzo; Stach, Eric A.; Jariwala, Deep; Olsson, Roy H.

doi:10.1002/pssr.202000575

Cited by 51 publications

(42 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has also been shown to be one of the most promising candidates for high-performance ferroelectric memory devices, scalable down to < 10 nm in thickness [18]. The AlScN films were characterized electrically and exhibit large coercive fields, E C , of 2-4.5 MV/cm [15][16][17][18][19]. This is important for scaling to thinner ferroelectric layers, all while maintaining a large memory window, high ON/OFF ratio and good retention [16,19].…”

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

“…S11). PUND testing is preferred over a polarization-electric field hysteresis loop (P-E loop) measurment because the P-E loop of 45 nm AlScN shows a polarization-dependent leakage which hinders the observation of polarization saturation for positive applied fields that switch the material into the metal-polar state [16][17]. The PUND result indicates a remanent polarization ~150 µC/cm 2 , as shown in Fig.…”

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

“…2a. AlScN is a recently discovered ferroelectric material with nearly ideal ferroelectric hysteresis loops, record values of remnant polarization, and a composition-tunable coercive field [15][16][17]. Furthermore, it can be integrated directly in a CMOS BEOL-compatible process technology over 8-inch wafers.…”

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

“…This is important for scaling to thinner ferroelectric layers, all while maintaining a large memory window, high ON/OFF ratio and good retention [16,19]. When combined with high measured remnant polarizations -P r of 80-150 μC/cm 2 [15][16][17][18][19] -this leads to a significant tunneling electro-resistance effect, based on strong tunnel barrier modulation, thus giving a high ON/OFF ratio (Supplementary Note 1). The field programmability, non-volatility and non-linearity of these devices can be leveraged for multiple, primitive data operations such as storage, search, and neural networks without the need for additional tansistors, as shown in b-d. b.…”

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

See 4 more Smart Citations

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

Liu

Ting

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data centric processing. At a hardware level, this presents an urgent need to integrate dense, high-performance and low-power memory units with Si logic-processor units. However, data-heavy problems such as search and pattern matching also require paradigm changing innovations at the circuit and architecture level to enable compute in memory (CIM) operations. CIM architectures that combine data storage yet concurrently offer low-delay and small footprint are highly sought after but have not been realized. Here, we present Aluminum Scandium Nitride (AlScN) ferroelectric diode (FeD) memristor devices that allow for storage, search and neural network-based pattern recognition in a transistor-free architecture. Our devices can be directly integrated on top of Si processors in a scalable, back-end-of-line process. We leverage the field-programmability, non-volatility and non-linearity of FeDs to demonstrated circuit blocks that can support search operations in-situ memory with search delay times < 0.1 ns and a cell footprint < 0.12 µm2. In addition, we demonstrate matrix multiplication operations with 4-bit operation of the FeDs. Our results highlight FeDs as promising candidates for fast, efficient, and multifunctional CIM platforms.

show abstract

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

Section: Field-programmable Alscn Feds For Memorymentioning

confidence: 99%

See 3 more Smart Citations

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

Liu

Ting

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Composition‐Graded Nitride Ferroelectrics Based Multi‐Level Non‐Volatile Memory for Neuromorphic Computing

Wang,

Ye,

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Multi‐level non‐volatile ferroelectric memories are emerging as promising candidates for data storage and neuromorphic computing applications, due to the enhancement of storage density and the reduction of energy and space consumption. Traditional multi‐level operations are achieved by utilizing intermediary polarization states, which exhibit an unpredictable ferroelectric domain switching nature, leading to unstable multi‐level memory. In this study, a unique approach of composition‐graded ferroelectric ScAlN to achieve tunable operating voltage in a wide range and attain precise control of domain switching and stable multi‐level memory is proposed. This non‐volatile memory supports multi‐level storage up to 7‐bit capacities, and exhibits enhanced performance compared to the uniform composition device, showing one order of magnitude higher ON/OFF ratio, 30% reduced working voltage, and up to 50% enhanced tuning window of operating voltage. Finally, the emulation of long‐term plasticity and linear weight update akin to biological synapse with high uniformity and reliability are demonstrated. The proposed composition‐grading architecture offers new opportunities for next‐generation multi‐level ferroelectric memories, paving the way for advanced hybrid integration in multifunctional computing systems.

show abstract

An Epitaxial Ferroelectric ScAlN/GaN Heterostructure Memory

Wang

Mondal

et al. 2022

Adv Elect Materials

View full text Add to dashboard Cite

Electrically switchable bistable conductance that occurs in ferroelectric materials has attracted growing interest due to its promising applications in data storage and in‐memory computing. Sc‐alloyed III‐nitrides have emerged as a new class of ferroelectrics, which not only enable seamless integration with III‐nitride technology but also provide an alternative solution for CMOS back end of line integration. In this paper, the resistive switching behavior and memory effect in an ultrawide‐bandgap, high Curie temperature, fully epitaxial ferroelectric ScAlN/GaN heterostructure is reported for the first time. The structure exhibits robust ON and OFF states that last for months at room temperature with rectifying ratios of 60–210, and further shows stable operation at high temperatures (≈670 K) that are close to or even above the Curie temperature of most conventional ferroelectrics. Detailed studies suggest that the underlying mechanism is directly related to a ferroelectric field effect induced charge reconstruction at the hetero‐interface. The robust resistive switching landscape and the electrical polarization engineering capability in the polar heterostructure, together with the promise to integrate with both silicon and GaN technologies, can pave the way for next‐generation memristors and further enable a broad range of multifunctional and cross‐field applications.

show abstract

Sub‐Microsecond Polarization Switching in (Al,Sc)N Ferroelectric Capacitors Grown on Complementary Metal–Oxide–Semiconductor‐Compatible Aluminum Electrodes

Cited by 51 publications

References 21 publications

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

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

Composition‐Graded Nitride Ferroelectrics Based Multi‐Level Non‐Volatile Memory for Neuromorphic Computing

An Epitaxial Ferroelectric ScAlN/GaN Heterostructure Memory

Contact Info

Product

Resources

About