Van der Waals Halide Perovskite Photo-ferroelectric Synapse

范德华卤化物钙钛矿光铁电突触

• 批准号: 1916652
• 负责人: Jian Shi
• 金额: $ 41.76万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-14 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916652&HistoricalAwards=false
• 关键词: Van; der; Waals; Halide; Perovskite

Nontechnical:Artificial intelligence is transforming our lives. Even at the earliest stages of commercialization, we have seen the power and potential of AI to support workers, diagnose diseases, and improve national security. Conventional digital computers are not efficient for brain-like (neuromorphic) computing such as artificial neural networks, which creates a tremendous power demands. The next stage of artificial intelligence requires devices and circuits that mimic biological processes. In particular, synaptic devices must be capable of learning in hardware. They must retain distinguishable states, be suitable for parallel computing and operate at high speed and low power. Previous attempts at such devices have been limited by disruption of the synapse memory during read operations. Essentially, the act of remembering can cause the synaptic device to forget. This award supports fundamental research to explore a new synaptic mechanism that circumvents this issue. These devices will be based on the ferroelectric materials, which have a spontaneous polarization that can be reversed by an applied electric field. This polarization can be read nondestructively by light, fulfilling the requirement for a synaptic device. These synaptic devices will be made from perovskites, materials with organic and inorganic components that are loosely bound by van der Waals forces. This approach will enable the realization of high-performance synaptic devices by informing the selection of materials, devices and circuits for neuromorphic computing. The results from this award will thereby benefit the U.S. economy, national security and health by enabling fundamental advances in the science and technology of artificial intelligence. The PI will also introduce and disseminate knowledge on materials and devices to enable AI by outreach to K-12 students.Technical:The research objective of this project is to explore, understand and exploit the synaptic memory property and characteristics in photo-ferroelectric devices based on van der Waals halide perovskites-based for the development of artificial synapses with designed plasticity. The key idea is to exploit the non-volatile photo-ferroelectric switching in high quantum efficiency multifunctional perovskites with devices in crossbar architecture. Van der Waals halide perovskites carry both ferroelectricity and semiconducting property from separate functional groups so one could decouple and optimize both properties. In this project, the PIs will predict and understand the intrinsic/extrinsic properties of materials proposed under external stimuli, and evaluate and understand the device characteristics and performance. The PIs will synthesize thin film high quantum efficiency halide perovskite materials, fabricate two-terminal artificial synapses/circuits, study atomic structures and fundamental physical properties, determine device performance including switching speed, retention time, dynamic window, number of distinguishable states, energy consumption per synaptic event, endurance, and test/optimize the synaptic device and circuit for simulating spike-timing-dependent plasticity. This work will therefore advance knowledge of fundamental material physics, synaptic switching dynamics as well as ideal circuit architectures of novel photo-ferroelectric devices based on layered halide perovskite materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术性：人工智能正在改变我们的生活。即使在商业化的最早阶段，我们也已经看到了人工智能在支持工人、诊断疾病和改善国家安全方面的力量和潜力。传统的数字计算机对于人工神经网络等类脑（神经形态）计算效率低下，这会产生巨大的功率需求。人工智能的下一阶段需要模仿生物过程的设备和电路。特别是，突触设备必须能够在硬件中学习。它们必须保持可区分的状态，适合并行计算，并以高速和低功耗运行。以前在这种设备上的尝试受到读取操作期间突触存储器中断的限制。从本质上讲，记忆的行为可以导致突触装置忘记。该奖项支持基础研究，以探索一种新的突触机制，以规避这一问题。这些器件将基于铁电材料，铁电材料具有可以通过施加电场逆转的自发极化。这种极化可以通过光非破坏性地读取，满足突触装置的要求。这些突触装置将由钙钛矿制成，这种材料含有有机和无机成分，通过货车德瓦尔斯力松散地结合在一起。这种方法将使高性能的突触设备的实现，通知神经形态计算的材料，设备和电路的选择。因此，该奖项的成果将通过促进人工智能科学技术的根本性进步，使美国经济、国家安全和健康受益。技术方面：本项目的研究目标是探索、理解和利用基于货车瓦卤化物钙钛矿的光铁电器件中的突触记忆特性和特征，以开发具有设计可塑性的人工突触。其核心思想是利用高量子效率多功能钙钛矿材料的非挥发性光铁电开关，器件采用交叉结构。货车瓦卤化物钙钛矿具有来自单独官能团的铁电性和半导体性质，因此可以解耦和优化这两种性质。在这个项目中，PI将预测和理解在外部刺激下提出的材料的内在/外在特性，并评估和理解器件的特性和性能。PI将合成薄膜高量子效率卤化物钙钛矿材料，制造双端人工突触/电路，研究原子结构和基本物理特性，确定设备性能，包括切换速度，保留时间，动态窗口，可区分状态的数量，每个突触事件的能耗，耐久性，并测试/优化突触设备和电路，以模拟尖峰定时依赖的可塑性。因此，这项工作将推进基础材料物理学，突触开关动力学以及基于层状卤化物钙钛矿材料的新型光铁电器件的理想电路架构的知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

van der Waals Ferroelectric Halide Perovskite Artificial Synapse

• DOI: 10.1103/physrevapplied.18.014014
• 发表时间: 2022-07
• 期刊: Physical Review Applied
• 影响因子: 4.6
• 作者: Yao Cai;Yang Hu;Zhizhong Chen;Jie Jiang;Lifu Zhang;Yuwei Guo;S. Pendse;Ru Jia;Jiahe Zhang-Jiahe

Unit-Cell-Thick Oxide Synthesis by Film-Based Scavenging

通过基于薄膜的清除法合成单胞厚氧化物

• DOI: 10.1021/acs.jpcc.0c00578
• 发表时间: 2020-03
• 期刊: J. Phys. Chem. C
• 作者: Saloni Pendse;Jie Jiang;Yuwei Guo;Lifu Zhang;Zhizhong Chen;Zonghuan Lu;Yu;ong Wang;Yang Hu;Songman Li;Jing Feng;Toh-Ming Lu;Yi-Yang Sun;Jian Shi
• 通讯作者: Jian Shi

A Van Der Waals Photo‐Ferroelectric Synapse

• DOI: 10.1002/aelm.202200326
• 发表时间: 2022-06
• 期刊: Advanced Electronic Materials
• 影响因子: 6.2
• 作者: Yao Cai;Lifu Zhang;Jie Jiang;Yang Hu;Zhizhong Chen;Ru Jia;Chengliang Sun;Jian Shi

Tuning phase transition kinetics via van der Waals epitaxy of single crystalline VO2 on hexagonal-BN

通过六方氮化硼上单晶 VO2 的范德华外延调整相变动力学

• DOI: 10.1016/j.jcrysgro.2020.125699
• 发表时间: 2020-08
• 期刊: Journal of Crystal Growth
• 影响因子: 1.8
• 作者: Saloni Pendse;Jie Jiang;Lifu Zhang;Yuwei Guo;Zhizhong Chen;Yang Hu;Zonghuan Lu;Songman Li;Jing Feng;Toh-Ming Lu;Jian Shi
• 通讯作者: Jian Shi

Unit-cell-thick domain in free-standing quasi-two-dimensional ferroelectric material

• DOI: 10.1103/physrevmaterials.5.044403
• 发表时间: 2021-04
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Yuwei Guo;B. Goodge;Lifu Zhang;Jie Jiang;Yu Chen;L. Kourkoutis;Jian Shi