High-Performance and CMOS-Compatible Electrochemical Random Access Memory For Neuromorphic Computing

用于神经形态计算的高性能且 CMOS 兼容的电化学随机存取存储器

• 批准号: 1950182
• 负责人: Qing Cao
• 金额: $ 42万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950182&HistoricalAwards=false
• 关键词: Performance; CMOS; Compatible; Electrochemical; Random

Artificial intelligence has made phenomenal progress in recent years. It is having a remarkable social impact with emerging applications such as face recognition and self-driving cars. However, such improvement comes with the cost of aggressively increased depth and size of the deep neural network models utilized, which leads to exponentially increasing computational load. This poses significant challenges for hardware implementations in terms of computation, memory, and communication resources. The objective of this project is to develop the next-generation neuro-inspired deep-learning hardware, which has potential to perform the data-intensive computation required by the artificial-intelligence algorithms with thousands times higher energy efficiency, compared to what is possible using current silicon complementary metal-oxide-semiconductor technology. The educational goal is to sustain STEM workforce pipeline development by exploiting the outreach opportunities and knowledge generated in the proposed project. Efforts will be to establish hands-on module for K-6 students to learn the difference between computer-based expert system and the human/machine learning process, as well as the working principles of artificial synapses for neuromorphic computing, with the purpose of introducing engineering to them. At the undergraduate level, PI proposes to incorporate case-analysis in engineering class, by capitalizing on PI’s industrial experiences. The target will be to help students develop the capability of using engineering judgement in decision-making regarding realistic technology development problems, which will have direct connection to what they learn in classroom.To achieve this objective, new types of high-performance and silicon complementary metal-oxide-semiconductor compatible electrochemical random access memories will be designed, fabricated, characterized, and optimized. These devices can serve as multi-level artificial synapses with near-symmetric weight update in response to pulsed input to dramatically accelerate the online training and the inference of deep neural networks. More specifically, two novel device prototypes will be explored in parallel during the grant term: one operates based on the resistance switch in a functional oxide channel modulated by the gate-controlled reversible insertion of protons from oxides with high ionic conductivity; the other is based on the resistance switch in multilayered two-dimensional semiconductors modulated by the gate-controlled intercalation of copper ions from fast ion-transporting metal-chalcogenide glass. A symmetric gate-channel stack will be adopted to minimize the drift of the device open-circuit potential during operation. The scientific goal of this project is to elucidate the correlation between the intercalant types, properties of the corresponding solid-state electrolytes and the intercalatable channels, device dimensions, and the electrochemical random access memory performance, using a combination of experiment and physics-driven device modeling. The technological goal is to move electrochemical random access memory from initial proof-of-concept demonstrations to a practical technology. Material innovations will firstly be applied on all the components across the device gate-channel stack to drastically enhance their performance, especially the device speed, retention, and endurance. Individual memory cells with sub-100 nm dimensions and 3 by 3 pseudo-crossbar arrays will then be demonstrated. These efforts will help us assess the technological promise of electrochemical random access memory, especially their ultimately achievable speed and their scalability into both nanoscale devices and large-scale integrated arrays.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.

人工智能近年来取得了惊人的进步。 随着人脸识别和自动驾驶汽车等新兴应用的出现，它正在产生显著的社会影响。 然而，这种改进伴随着所使用的深度神经网络模型的深度和大小的急剧增加的成本，这导致计算负载呈指数级增加。 这在计算、存储器和通信资源方面对硬件实现提出了重大挑战。该项目的目标是开发下一代神经启发的深度学习硬件，该硬件有可能执行人工智能算法所需的数据密集型计算，与使用当前硅互补金属氧化物半导体技术相比，其能效高出数千倍。教育目标是通过利用拟议项目中产生的外联机会和知识来维持STEM劳动力管道的发展。我们将努力为K-6学生建立实践模块，学习基于计算机的专家系统与人类/机器学习过程之间的区别，以及神经形态计算的人工突触的工作原理，目的是向他们介绍工程学。在本科阶段，PI建议通过利用PI的工业经验，将案例分析纳入工程类。本课程的目标是培养学生运用工程学的判断力，对实际的技术发展问题作出决策，这与他们在课堂上学到的知识有直接的联系。为了实现这一目标，将设计、制造、表征和优化新型的高性能硅互补金属氧化物半导体兼容电化学随机存取存储器。 这些设备可以作为多级人工突触，响应于脉冲输入进行近似对称的权重更新，从而大大加速深度神经网络的在线训练和推理。 更具体地说，两个新的器件原型将在资助期间并行探索：一个是基于功能氧化物通道中的电阻开关，该通道由来自高离子电导率氧化物的质子的门控可逆插入调制;另一种是基于多层二维半导体中的电阻开关，该电阻开关由来自快离子传输金属的铜离子的门控嵌入调制，硫系玻璃 将采用对称的栅极沟道堆叠以最小化操作期间器件开路电位的漂移。 该项目的科学目标是阐明嵌入剂类型，相应的固态电解质和可嵌入通道，器件尺寸和电化学随机存取存储器性能之间的相关性，使用实验和物理驱动的器件建模相结合。 其技术目标是将电化学随机存取存储器从最初的概念验证演示转变为实用技术。 材料创新将首先应用于器件栅极-沟道堆叠的所有组件，以大幅提高其性能，特别是器件速度、保持力和耐久性。 然后将演示具有亚100纳米尺寸和3乘3伪交叉阵列的单个存储单元。 这些努力将帮助我们评估电化学随机存取存储器的技术前景，特别是它们最终可实现的速度和它们在纳米级器件和大规模集成阵列中的可扩展性。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。

项目成果

CMOS-compatible electrochemical synaptic transistor arrays for deep learning accelerators

• DOI: 10.1038/s41928-023-00939-7
• 发表时间: 2023-03-27
• 期刊: NATURE ELECTRONICS
• 影响因子: 34.3
• 作者: Cui, Jinsong;An, Fufei;Cao, Qing
• 通讯作者: Cao, Qing