CAREER: Scalable Ionic Gated 2D Synapse (IG-2DS) with Programmable Spatio-Temporal Dynamics for Spiking Neural Networks

职业生涯：可扩展离子门二维突触(IG-2DS)和用于尖峰神经网络的可编程时空动力学ECCS：1943683 PI：冯雄非技术：人工智能(AI)具有改变人们生活的潜力。机器学习在医疗保健、交通运输和先进制造业中显示出了希望，但需要巨大的能量。人脑在模式识别等认知任务上比超级计算机做得更好，而所需功率不到20W。在人脑的启发下，神经形态计算和人工神经网络最近吸引了巨大的兴趣。尖峰神经网络通过结合相似的时间动力学来模拟生物过程。这种类型的计算为高带宽的节能计算提供了一条很有前途的途径。然而，使用现有的数字电子设备在尖峰神经网络中实现时空过程，例如短期和长期记忆，是具有挑战性和昂贵的。在这个项目中，PI将通过控制二维设备中的电荷载流子浓度来开发一个严重缺失的元素--动态突触。这一突破将带来一个真正的神经现实计算系统，在能源效率、带宽和认知能力方面都有显著的改进。这可以导致人工智能的广泛使用，并通过认知计算、自动驾驶汽车和自动制造的进步来彻底改变社会。PI将与当地社区参与中心合作开发一项课后外展计划。来自代表性不足群体的学生将拥有设计和实验室经验，目的是吸引他们进入工程职业。技术：本项目的目标是阐明离子门二维突触(IG-2DS)设备中的传输机制，并为尖峰神经网络(SNN)的硬件实现构建具有可编程时空动力学、高精度、低功耗、良好可扩展性和良好可靠性的突触阵列。尽管最近在人工神经网络的开发方面取得了成功，但SNN的硬件实现一直具有挑战性，因为现有的数字电子设备不具备动态突触所需的时空动力学--动态突触是SNN的关键组成部分。在这个项目中，PI将采用一种新的方法来证明2D突触的短期和长期可塑性，方法是通过嵌入来调制来自离子门的挥发性掺杂效应和来自电荷转移掺杂的非挥发性掺杂效应。PI将执行以下三个研究任务：(1)阐明IG-2DS的短期和长期掺杂机制；(2)为SNN的硬件实现演示IG-2DS中可调的时空动力学；(3)研究IG-2DS用于大规模集成的缩放潜力。从根本上讲，这项工作阐明了二维电子学中的电子和离子输运。实际上，这项工作将通过SNN的硬件实施在计算、自动驾驶、制造自动化、柔性传感器和可穿戴电子设备领域产生巨大影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

CAREER: Scalable Ionic Gated 2D Synapse (IG-2DS) with Programmable Spatio-Temporal Dynamics for Spiking Neural NetworksECCS : 1943683PI: Feng XiongNontechnical:Artificial intelligence (AI) has the potential to transform people's lives. Machine learning has shown promise in healthcare, transportation, and advanced manufacturing, but requires enormous amounts of energy. The human brain is better at cognitive tasks such as pattern recognition than even supercomputers while requiring less than 20W of power. Inspired by the human brain, neuromorphic computing and artificial neural networks have recently attracted immense interest. Spiking neural networks mimic biological processes by incorporating similar temporal dynamics. This type of computing offers a promising route for energy-efficient computing with high bandwidth. It is, however, challenging and expensive to implement spatio-temporal processes such as short-term and long-term memory in spiking neural networks with existing digital electronics. In this project, the PI will develop a critically missing element, a dynamic synapse, by controlling the charge carrier concentration in two-dimensional devices. This breakthrough will lead to a truly neuro-realistic computing system with dramatic improvements in energy efficiency, bandwidth, and cognitive capabilities. This can lead to the wide use of AI and revolutionize society through advances in cognitive computing, self-driving vehicles, and autonomous manufacturing. The PI will develop an afterschool outreach program in partnership with a local community engagement center. Students from underrepresented groups will have design and laboratory experiences with the aim of attracting them into engineering careers.Technical:The objective of this project is to elucidate the transport mechanisms in ionic gated two-dimensional synaptic (IG-2DS) devices and build synaptic arrays with programmable spatio-temporal dynamics, high precision, low power, good scalability, and good reliability for the hardware implementation of spiking neural networks (SNNs). Despite recent success in the development of artificial neural networks, the hardware implementation of SNNs has been challenging because existing digital electronics do not possess the spatio-temporal dynamics needed for a dynamic synapse-the key building block of SNNs. In this project, the PI will adopt a novel approach to demonstrate short-term and long-term plasticity in 2D synapses by modulating the volatile doping effect from ionic gating and the non-volatile doping effect from charge transfer doping via intercalation. The PI will carry out the following three research tasks: (1) elucidate the short- and long-term doping mechanisms in IG-2DS; (2) demonstrate tunable spatio-temporal dynamics in IG-2DS for the hardware implementation of SNNs; and (3) investigate the scaling potentials of IG-2DS for large-scale integration. Fundamentally, this work elucidates the electronic and ionic transport in 2D electronics. Practically, this work will have an immense impact in the fields of computing, self-driving, automation in manufacturing, flexible sensors, and wearable electronics through the hardware implementation of SNNs.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.