Synaptic dynamics in ferroelectric devices and their application to deep neural networks

非技术性：现代计算机时代的宏伟愿景之一就是模仿人脑的认知能力，甚至与之匹敌。由于机器学习和人工智能的最新进展，这一愿景正在成为可能。目前的计算技术还远远没有创造出一个像生物大脑一样有能力和能源效率的数字实体。众所周知，数字学习系统非常耗电，可能需要满屋子的数字计算机集群。相比之下，人类的大脑在20瓦的微薄功率预算和不到两公斤的重量下完成了所有的壮举。这种低效率的原因之一是晶体管，数字计算机的基本组成部分，不像突触，生物计算的基础，以同样的方式工作。拟议的研究旨在通过对晶体管的结构进行相对基本的改变来克服这一障碍。一种具有铁电特性的新兴材料，掺杂氧化铪，将被引入晶体管。这种新器件被称为铁电场效应晶体管，可以模拟生物突触的特性。在这个项目中，突触铁电晶体的独特性质将被用于设计和优化人工智能核心，如深度神经网络，大大超过当前最先进的性能和效率。该项目将在涉及材料科学，电路设计，计算机架构和神经科学的跨学科环境中培养参与学生。STEM推广和教育计划将帮助参与的本科生、高中生和高中教师拓宽他们在计算机科学和新型半导体器件方面的经验。技术：该项目将探索铁电氧化硅-氧化锆合金门控硅晶体管中丰富的畴动力学，以构建矢量矩阵乘法交叉杆的突触单元。架构和系统级工作将需要基于这些铁电交叉杆内核的成熟深度神经网络的设计和优化。基于物理的铁电晶体的紧凑模型，占域动力学的重要细节将开发，这将连接材料-器件级的工作和架构-系统级的工作。该项目的一个关键特征是其垂直集成方法，涉及从材料到系统的计算层次结构中的不同级别。所有这些不同层面的创新将确保新兴铁电器件技术的有趣特性可以被充分利用，为先进的机器学习和数据密集型认知应用创建一个节能和高性能的硬件平台。该奖项反映了NSF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:One of the grand visions of the modern computing era has been to mimic the cognitive capabilities of the human-brain, and even to rival them. This vision is becoming possible due to recent advances in machine learning and artificial intelligence. Current computing technologies are still far from creating a digital entity that is as capable and as energy efficient as a biological brain. Digital learning systems are notoriously power hungry and can require a room-full of digital computer clusters. Compare that to the human brain which performs all its feats at a meager power budget of twenty watts and a weight of less than two kilograms. One reason for this inefficiency is that transistors, the basic building blocks of digital computers, do not function in the same way as synapses, the basis of biological computing. The proposed research aims at overcoming this barrier by making a relatively basic change to the structure of the transistor. An emerging material with ferroelectric properties, doped hafnium oxide, will be introduced into transistors. The new device is called a ferroelectric field effect transistor and can emulate the properties of biological synapses. In this project, the unique properties of the synaptic ferroelectric transistor will be used to design and optimize artificial intelligence cores such as deep neural networks that vastly exceed the performance and efficiency of the current state-of-the-art. The project will train participating students in an interdisciplinary setting that involves material science, circuit design, computer architecture, and neuro-science. The STEM outreach and education programs will help participating undergraduates, high school students and high school teachers to broaden their experience in computer science and novel semiconductor devices.Technical:The project will explore the rich domain dynamics in ferroelectric hafnia-zirconia alloy gated silicon transistors to build synaptic units for vector matrix multiplication crossbars. The architecture and system level work will entail the design and optimization of full-blown deep neural networks based on these ferroelectric crossbar kernels. Physics based compact models of ferroelectric transistors that account for the important details of domain dynamics will be developed which will tie the material-device level work and the architecture-system level work. A key feature of the project is its vertically integrated approach that involves different levels in the computing hierarchy from materials to systems. Innovations at all these different levels will ensure that the interesting properties of the emerging ferroelectric device technology can be fully leveraged to create an energy efficient and high-performance hardware platform for advanced machine learning and data intensive cognitive applications.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.