FuSe-TG: Co-design of Attojoule Multifunction Semiconductor Electronics with Atomic Precision

未来的半导体系统将面临巨大的挑战，因为人工智能（AI）、机器学习（ML）和集成数据和计算的分布式边缘应用程序的广泛采用推动了数据处理的大规模扩展。根据2021年半导体研究公司的十年计划，将计算的能源效率提高数百万倍势在必行。一个令人信服的解决方案是使半导体器件更有能力，即，多功能的具有带隙的材料不一定使其成为半导体，直到存在杂质离子，即掺杂剂，以调节其电导和其他性质。半导体中掺杂剂离子的移动打开了一扇几乎无限机会的窗户。未来的半导体可能会在创纪录的低阿托焦耳（10-18焦耳）能级上以原子精度动态重新配置，这类似于但优于生物突触系统。这笔赠款将组建一个团队，为具有原子精度的阿托焦耳半导体材料，多功能设备和智能系统开发模拟和人工智能指导的协同设计框架，用于未来的超低功耗计算和存储器，从而实现无处不在的人工智能的可持续发展社会。教育目标是建立一个跨学科的联盟，培养下一代的半导体网络人才，他们将通过在高端计算，量子计算和人工智能的关系中创新地使用先进的网络基础设施来解决具有挑战性的材料-设备-系统协同设计问题。该资助旨在建立一个可重构的多功能突触切换的颠覆性范式，其能量消耗和原子精度，从而为节能边缘计算创造了一种新的工业方法。主要创新包括：（1）确定性的质子电化学离子突触，以阿托焦耳能量消耗实现高速开关;（2）在边缘无需外部电源的情况下处理图像的传感器内计算系统;（3）基于第一性原理的多尺度模拟和AI指导的Synaptic材料-器件-系统协同设计，从而为半导体（FuSe）的未来提供可推广的理性协同设计框架。基础软件套件被开发成可通过CyberFuSe门户访问的CyberFuSe培训模块。培训模块在课堂上进行试点，以支持双学位课程（博士）。在物理学，材料科学或电气工程与计算机科学或人工智能硕士），并在网络教育培训讲习班任教，重点关注代表性不足的群体。此外，该补助金还为社区学院的学生提供职业途径，社区学院的学生占全国本科生的三分之一以上。该补助金还通过以下方式扩大了参与：（1）南加州大学的科学和工程妇女（WiSE）计划;（2）由南加州大学、麻省理工学院、斯坦福大学、CMU、TAMU和霍华德（历史上最古老、最大的黑人学院和大学之一）的教师共同监督的代表性不足的群体的本科生研究。FuSe团队包括来自SLK America的工业合作伙伴，应用材料公司和IBM的加速技术转让和研究和未来劳动力需求的反馈。该项目是由半导体的未来（FuSe）计划和历史上的黑人学院和大学本科生计划（HBCU-UP）联合资助。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。

Future semiconducting systems will face formidable challenges due to massive expansion of data processing that is driven by the broad adoption of artificial intelligence (AI), machine learning (ML), and distributed Edge applications that integrate data and computing. It is imperative to increase the energy efficiency of computing million-fold, according to the 2021 Semiconductor Research Corporation decadal plan. A compelling solution is to make semiconductor devices more capable, i.e., multifunctional. A material with a bandgap does not necessarily make it a semiconductor until there are impurity ions, namely dopants, to tune its electrical conductance and other properties. Mobilizing the dopant ions in semiconductors opens a window with nearly infinite opportunities. It is likely that future semiconductors will be dynamically reconfigurable on the fly with atomic precision at record-low attoJoule (10-18 Joule) energy level, which resembles but outperforms biological synaptic systems. This grant is to forge a team for developing a simulation and AI guided co-design framework for attoJoule semiconductor materials with atomic precision, multifunctional devices, and smart systems for future ultralow-power computing and memory, thereby realizing a sustainable society with ubiquitous AI. The educational goal is to establish a cross-disciplinary coalition that trains a future generation of semiconductor cyberworkforce, who will solve challenging material-device-system co-design problems through innovative use of advanced cyberinfrastructure at the nexus of high-end computing, quantum computing and AI.The grant aims to establish a disruptive paradigm for reconfigurable multifunctional synaptic switching with aJ energy consumption and atomic precision, thereby creating a new industrial approach for energy-efficient Edge computing. Key innovations include: (1) Protonic electrochemical ionic synapse that is deterministic and achieves high-speed switching with attoJoule energy consumption; (2) In-sensor computing systems to process images without external power supply at Edge; (3) Synaptic material-device-system co-design guided by first principles-based multiscale simulation and AI, thus providing a generalizable rational co-design framework for the future of semiconductors (FuSe). The underlying software suite is developed into a CyberFuse training module that is accessible through a CyberFuSe portal. The training modules are piloted in classrooms to support a dual-degree program (Ph.D. in physics, materials science or electrical engineering with MS in computer science or AI) and taught in CyberFuse training workshops with a strong focus on underrepresented groups. In addition, the grant provides career pathways to community college students, who constitute more than one third of the nation’s undergraduate students. The grant also broadens participation through (1) USC’s Women in Science and Engineering (WiSE) program and (2) undergraduate research by underrepresented groups jointly supervised by faculty from USC, MIT, Stanford, CMU, TAMU and Howard - one of the oldest and largest historically black colleges and universities. The FuSe team includes industrial partners from SLK America, Applied Materials and IBM for accelerated technology transfer and feedback on research and future workforce needs.This project is jointly funded by the Future of Semiconductors (FuSe) program and by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP).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.