Van der Waals Halide Perovskite Photo-ferroelectric Synapse

非技术：人工智能正在改变我们的生活。即使在商业化的最初阶段，我们也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）计算的效率不高，这产生了巨大的功率需求。人工智能的下一个阶段需要模拟生物过程的设备和电路。特别是，突触装置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，并在高速和低功耗下运行。以前对这种设备的尝试一直受到读取操作期间突触记忆中断的限制。从本质上讲，记忆的行为会导致突触装置遗忘。该奖项支持探索一种新的突触机制的基础研究，以规避这一问题。这些装置将以铁电材料为基础，铁电材料具有自发极化，可以通过外加电场逆转。这种偏振可以被光无损地读取，满足了突触装置的要求。这些突触装置将由钙钛矿制成，钙钛矿是一种由范德华力松散束缚的有机和无机成分组成的材料。这种方法将通过为神经形态计算提供材料、设备和电路的选择，从而实现高性能突触设备。因此，该奖项的成果将通过推动人工智能科学和技术的根本性进步，使美国经济、国家安全和健康受益。PI还将向K-12学生介绍和传播有关人工智能的材料和设备的知识。技术：本项目的研究目的是探索、了解和利用基于范德华卤化物钙钛矿的光铁电器件的突触记忆特性和特征，开发具有设计可塑性的人工突触。其关键思想是利用跨栅结构器件在高量子效率多功能钙钛矿中实现非易失性光铁电开关。范德华卤化物钙钛矿从不同的官能团携带铁电性和半导体性质，因此可以解耦并优化这两种性质。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性质，确定器件性能，包括开关速度，保持时间，动态窗口，可区分状态数，每个突触事件的能量消耗，耐久性，并测试/优化突触器件和电路，以模拟峰值时间依赖的可塑性。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结构的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.