Primary Awardee: University of Missouri Kansas City

PI: Farid Nait Abdusselam, Yugyung  Lee (Co-Principal Investigator), Dianxiang  Xu (Co-Principal Investigator), Masud  Chowdhury (Co-Principal Investigator), Mostafizur  Rahman (Co-Principal Investigator)

Overview: Alcohol and drug abuse are on the rise around the world, causing serious socioeconomic crises and consequences, such as accidents, homicides, suicides, physical and sexual assaults, or other problems such as anxiety and depression. Existing data for the treatment of alcohol and drug abuse are primarily collected through unreliable self-reports. Other methods are direct measurements on bodily fluids, breath, or sweat, which require penetration through the skin or physical maneuvering, and indirect estimates through interviews and observations. Non-invasive sensing and testing paired with artificial intelligence (AI) techniques offer the promise of vastly improved data collection methods and analysis that in turn can improve treatments. However, the world, and the United States in particular, faces a shortage of trained workforce and innovators in this field. This NSF Research Traineeship (NRT) award will address these needs by preparing master’s and doctoral students to make convergent research contributions in the interdisciplinary fields of artificial intelligence (AI) and secure networked sensing to tackle the growing alcohol and drug crisis, and other social pandemics. The project will provide a unique and comprehensive training opportunity for up to one hundred twenty graduate students, including twenty funded trainees, by combining the disciplines of AI, communication networks, cybersecurity, sensor technologies, interactions with healthcare and legal partners, and a culture of innovation and translational research that considers human factors, law, regulation, communication, and leadership.

This project examines the global and national alcohol/drug abuse crisis from a broad perspective, including technological, ethical, legal, and socioeconomic challenges and opportunities. It aims at making radical programmatic changes in the way traditional and non-traditional graduate students are trained. This crisis, which costs in work productivity, healthcare, and law enforcement expenses, requires interdisciplinary research to efficiently collect and assemble data, possibly from diverse sources, and analyze it to extract actionable information while being conscious of contextual factors, such as a consumer’s health history, potential criminal record, personal background, and individual data security and privacy. The data aspects of this societal problem pose significant challenges on its collection, e.g., non-invasive sensing, and its secure and privacy-aware data analytics, which are the main research lines of this proposal. The project’s students will get practical and multidisciplinary training to prepare them as future skilled professionals in the rapidly growing area of applications of AI and secure networked sensing technologies. The proposed model combines project-based learning techniques, multidisciplinary programs, ethics, civic engagement, leadership, and professional development and dissemination initiatives. The educational model structure will allow students from seven interdisciplinary doctoral and four master’s programs to participate in the activities.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

Duration: 5 Years.

Primary Awardee: University of Missouri Kansas City

PI: Mostafizur Rahman

Overview: The goal of the team is to seek commercialization potentials of a breakthrough research in nanoscale computing leveraging interconnects for future digital Integrated Circuits (ICs). As traditional way of miniaturization of computer chips reaches unsurmountable barriers at sub-10nm technology nodes, the proposed technology provides pathways to continue scaling with transformative benefits. With innovations in several layers from fundamental physical components, computing model, to circuits and integration, the technology not only promises denser, faster and more power efficient chips but will also provide unique solutions for fault-tolerant and secure hardware through inherent physical capabilities and design choices. The solution will be beneficial for a wide range of customers from microprocessor producers to consumer electronics manufacturers and be particularly attractive for defense entities. The outcome of the proposed activity is a customer discovery and assessment of commercialization potentials.

The intellectual merit of the I-Corps teams research is the exploration of a novel computing technology that relies on deterministic interference between adjacent nanoscale interconnects (Crosstalk) for logic computing. The proposed approach departs from device switching dependent computing paradigm and relaxes difficult device scaling requirements. The scalability in Crosstalk fabric is determined primarily by circuit scheme, integration and the ability to pattern smaller metal nano-lines and deposit dielectrics in between them, which can be done by utilizing existing EDA and manufacturing methods. The proposed computing approach is functionally complete, and provides huge opportunities for logic reduction; by having more than 2 inputs couple to a single output and by varying their respective coupling capacitances, a logic implementation (e.g., Carry logic for Addition) that would typically require more than 15 transistors in CMOS, can be done by just 5 transistors. Another distinct feature is the run-time reconfigurability that allows different functionalities to be embedded in the same circuit (i.e., an adder unit can act as a multiplier/sorter). This allows re-usability and resource sharing, which can be transformative for fault tolerance (i.e., if a portion of CPU is damaged, the functioning portion can be configured to do both tasks), and circuit camouflaging for cybersecurity.

Duration: 1.5 Years.

Primary Awardee: University of Missouri Kansas City

PIs: Masud Chowdhury, Ahmed Hassan, Mostafizur Rahman

Overview: We propose to develop new infrastructure for computer aided design (CAD) simulations, experimental metrology, and software and hardware calibrations to support cross-layer evaluation of novel nanoscale 3-D Integrated Circuits for beyond CMOS, heterogonous 3-D integration for ambient intelligence, and nanoscale sensor technologies for nanocomposites, and health-care applications. Acquisition of new equipment including ultra-high resolution Scanning Electron Microscope (SEM), probe station and 8 micromanipulators are proposed, along with computing hardware, and software resources that include 4 high-end XEON servers, 5 workstations, and COMSOL, Sentaurus Lithography and Topography CAD tools. Sustainment of existing infrastructures: high bandwidth network analyzer, Matlab, Sentaurus Process, Device, Visual, and Raphael, Sunopsys DC, PrimeTime, HSPICE and CSCOPE, and Cadence Encounter and Virtuoso are also proposed. The proposed acquisitions and sustainments along with existing fabrication infrastructure will allow experimental patterning, deposition, etching, imaging, electrical, thermal and magnetic characterizations, as well as theoretical modeling and simulation of lithography, process, material, device, circuit, interconnect and large-scale architecture. These resources will be integrated in a CAD development and experimental metrology testbed that will allow end-to-end modeling, simulations, fabrication, characterization and calibrations, and enable breakthrough research in the areas of post-CMOS computing with fine-grained 3-D sequential integration of silicon and 2-D material based technologies, ambient intelligence with heterogeneous 3-D integration of mixed-signal CMOS and 2-D material based bio-sensors in a single chip, and nanoscale sensor technologies with Graphene and Carbon nanotubes. In addition to the new research directions proposed, the testbed will also greatly help big data analytics, bio-informatics, renewable energy, electromagnetics, and green city research directions within the CISE discipline at UMKC. This proposal comes with full institutional support and a detailed multi-year management plan for purchase, installation, usage and regular inspection of the infrastructure

Duration: June 2016 to June 2019