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

University Of Southern California, Los Angeles CA

Investigators

Abstract

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. 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.