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PCL-TEST BED: LAYERED AND THIN-FILM TECHNOLOGIES WITH INTELLIGENT CLOUD EXPERIMENTATION (LATTICE) -PCL-TEST BED: LAYERED AND THIN-FILM TECHNOLOGIES WITH INTELLIGENT CLOUD EXPERIMENTATION (LATTICE) THE LAYERED AND THIN FILM TECHNOLOGIES WITH INTELLIGENT CLOUD EXPERIMENTATION (LATTICE) PROGRAMMABLE CLOUD LABORATORY WILL CREATE A NEW ARTIFICIAL INTELLIGENCE (AI)-ENABLED USER FACILITY TO ACCELERATE THE DISCOVERY AND DEVELOPMENT OF ADVANCED SEMICONDUCTOR AND QUANTUM MATERIALS. BY COMBINING AUTOMATED RESEARCH TOOLS, REMOTE CLOUD ACCESS, AND AI-GUIDED EXPERIMENTATION, LATTICE WILL HELP RESEARCHERS MORE QUICKLY AND RELIABLY DEVELOP THIN-FILM MATERIALS NEEDED FOR NEXT-GENERATION COMPUTING, SENSING, COMMUNICATIONS, AND QUANTUM TECHNOLOGIES AND ACCELERATE THEIR DEPLOYMENT. THE PROJECT WILL EXPAND ACCESS TO STATE-OF-THE-ART RESEARCH INFRASTRUCTURE FOR SCIENTISTS AND STUDENTS ACROSS THE COUNTRY WHILE PROMOTING COLLABORATION AND DATA SHARING AMONG UNIVERSITIES, GOVERNMENT LABORATORIES, AND INDUSTRY. LATTICE WILL ALSO PROVIDE HANDS-ON AND REMOTE WORKFORCE TRAINING IN SEMICONDUCTOR MANUFACTURING, AI-DRIVEN RESEARCH, AND ADVANCED MATERIALS SCIENCE, HELPING PREPARE STUDENTS FOR CAREERS IN EMERGING HIGH-TECHNOLOGY INDUSTRIES. BY MAKING ADVANCED MATERIALS RESEARCH MORE ACCESSIBLE, COLLABORATIVE, REPRODUCIBLE AND PROGRAMMABLE, THE PROJECT WILL STRENGTHEN U.S. LEADERSHIP IN SEMICONDUCTOR INNOVATION, QUANTUM SCIENCE, AND THE TECHNOLOGY WORKFORCE. LATTICE WILL ESTABLISH A PROGRAMMABLE CLOUD LABORATORY NODE TO ACCELERATE DISCOVERY AND DEVELOPMENT OF THIN-FILM MATERIALS FOR NEXT GENERATION SEMICONDUCTOR AND QUANTUM INFORMATION TECHNOLOGIES. LEVERAGING EXISTING PHYSICAL AND DATA INFRASTRUCTURE, LATTICE WILL INTEGRATE MOLECULAR BEAM EPITAXY, METALORGANIC CHEMICAL VAPOR DEPOSITION, AND SPUTTER DEPOSITION TOOLS WITH REAL-TIME IN SITU DIAGNOSTICS, MULTIMODAL CHARACTERIZATION, AND AI-GUIDED EXPERIMENTAL ORCHESTRATION. A HYBRID EDGE-TO-CLOUD ARCHITECTURE WILL ENABLE REMOTE OPERATION, SCALABLE ANALYTICS AND REPRODUCIBLE WORKFLOWS ACROSS DISTRIBUTED INSTRUMENTATION FOR CLOSED-LOOP AUTONOMOUS EXPERIMENTATION. A MULTI-AGENT AI FRAMEWORK WITH PLAN?MEMORY?EXECUTE?REFINE AGENTS WILL COORDINATE EXPERIMENT PLANNING, EXECUTION, ANALYSIS, AND ADAPTIVE REFINEMENT USING MULTIMODAL DATA STREAMS AND DIGITAL TWIN SIMULATIONS. LATTICE WILL EMPLOY CROSS-MODAL MACHINE LEARNING LINKING ABUNDANT LOW-FIDELITY DATA WITH SPARSE HIGH-FIDELITY DATA TO ESTABLISH PREDICTIVE SYNTHESIS MODELS AND PROCESS?STRUCTURE?PROPERTY RELATIONSHIPS. THESE CAPABILITIES WILL BE DEMONSTRATED THROUGH ACCELERATED DEVELOPMENT OF SEMICONDUCTOR, FERROELECTRIC, AND QUANTUM THIN-FILM MATERIALS IN WAFER-SCALE, MANUFACTURING-COMPATIBLE SYSTEMS. LATTICE WILL EXPAND ACCESS TO ADVANCED THIN-FILM RESEARCH INFRASTRUCTURE THROUGH CLOUD-ENABLED EXPERIMENTATION AND FINDABLE, ACCESSIBLE, INTEROPERABLE, REUSABLE (FAIR)-ALIGNED DATA AND WORKFLOW SHARING. THE PROJECT WILL LEVERAGE ESTABLISHED USER NETWORKS FOR TESTING AND FEEDBACK, SUPPORT COLLABORATIONS AMONG UNIVERSITIES, NATIONAL LABORATORIES AND INDUSTRY ACROSS THE COUNTRY, AND FACILITATE COORDINATED DEVELOPMENT WITH OTHER PCL NODES. HANDS-ON STUDENT INTERNSHIPS AND DEPLOYMENT OF OPEN EDUCATIONAL RESOURCES WILL TRAIN THE NEXT GENERATION OF RESEARCHERS IN AI-ENABLED MATERIALS SYNTHESIS AND AUTOMATED/AUTONOMOUS EXPERIMENTATION. BY INTEGRATING ADVANCED CYBERINFRASTRUCTURE, AI-GUIDED DISCOVERY, AND COMMUNITY ENGAGEMENT, LATTICE WILL ESTABLISH A SCALABLE NATIONAL MODEL FOR INTELLIGENT, NETWORKED MATERIALS RESEARCH INFRASTRUCTURE. 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.- SUBAWARDS ARE PLANNED FOR THIS AWARD.

Cooperative Agreement (B) · National Science Foundation · to The Pennsylvania State University (UEI NPM2J7MSCF61) · data as of 2026-09-30

Source: USAspending.gov prime award transactions and FSRS subaward reports, as loaded by SpendQuery (data as of 2026-09-30). Amounts are obligations (money committed), not outlays. The official record on USAspending.gov ↗