1. UNCLASSIFIED Fundamental Aspects of Radiation Event Generation for Electronics and Engineering Research Robert A. Weller Institute for Space and Defense Electronics School of Engineering Vanderbilt University

2. UNCLASSIFIED Overview Introduction Research Program Background Technical Objectives Approach Expected Research Results Technology Transfer

3. UNCLASSIFIED Introduction The Question: 1.If you model all the physical processes by which radiation interacts with materials, and by which electric charge moves in solids, as accurately as current knowledge permits, can you predict radiation effects in semiconductor devices from first principles? 2.If not, then why not? The essential issue - Complexity!

4. UNCLASSIFIED Background Existing methods for predicting the rate of single event upsets in semiconductor devices have begun to fail because their of their basic assumptions. New computational methods developed at Vanderbilt have exposed gaps in basic science that limit our ability to make accurate predictions of single event effects. Two important areas needing basic work are: The generation of final states of ionizing particles following nuclear reactions. The microstructure of energy deposition and charge generation by ions, including the spatial and energy distributions of carriers. The detailed motion of charge in matrices of sub 100 nm structures is also important but is not the primary focus of this work.

5. UNCLASSIFIED SEU Rate for a Modern SRAM Comparing a calculation with data from a real SRAM flown by NASA Observed Average SEU Rate: 1x10 -9 Events/Bit/Day Vendor predicted rate using CREME96: 2x10 -12 Events/Bit/Day Classical Method nearly a factor 500 lower than the observed rate VU-ISDE rate: All relevant physics with Geant4 1.3x10 -10 to 1.3x10 -9 Errors/Bit/Day Wide error bar from Geant4 ion-ion physics uncertainty Multi- layered Stack

6. UNCLASSIFIED The Impact of Complexity NMOSPMOSNMOS Kevin Warren, VU ISDE 12-T DICE Latch

7. UNCLASSIFIED A Focus on the Nuclear Physics

8. UNCLASSIFIED Charge Generation TiN 0.1 µm Si 0.25 µm SiO2 0.6 µm Al 0.45 µm SiO2 0.60 µm Al 0.84 µm SiO2 1.0 µm Si-Nitride 0.4 µm 50 µm SiO2 or W 0.6 µm TiN 0.1 µm 0 Nuclear Reactions

9. UNCLASSIFIED Charge Motion 10 18 e - /cm 3 10 14 e - /cm 3

10. UNCLASSIFIED Technical Objectives Establish the available data, theory and computation for ion-ion nuclear reactions Build virtual experiments that test these models for best fit to semiconductor data Identify the best available nuclear reaction codes Interface and/or adapt the best codes for MRED Improve the nuclear-physics/charge-transport interface as necessary for < 100 nm structures Establish a roadmap for any necessary nuclear reaction research

11. UNCLASSIFIED Approach - Strategy Identify the best available physics. Code the physics (whenever possible) in timeless algorithms. Use supercomputer-based high- fidelity simulations to extract the physics that arises from complexity. Establish validity by comparison with experimental data. Let computer evolution deal with any power shortfall (if possible). Croc image: http://crocodilian.com/crocfaq/

12. UNCLASSIFIED Approach - Implementation Identify available nuclear reaction models. Adapt models to mred/Geant4 if necessary. Develop structures to simulate nuclear physics and radiation effects experiments. Conduct simulated experiments for comparison with experimental data. Identify the best available models for practical applications. Identify essentially-related issues outside the scope of this effort.

13. UNCLASSIFIED Manage Complexity with Computing

14. UNCLASSIFIED Predicting Experiments

15. UNCLASSIFIED The Structure of MRED Python: The common system language. MRED: A Python module called mredPy. Target: VU Linux cluster. Python writes submission scripts, controls job execution, and merges results. Geant4 MRED C++ SWIG Python

16. UNCLASSIFIED Expected Results Identify optimum nuclear modes for MRED. Validate nuclear models against semiconductor data. Identify issues related to the interface between charge generation and transport in sub-100 nm structures. Deeper understanding of the intricacies of charge generation. Reduced uncertainty in SEU prediction.

17. UNCLASSIFIED Technology Transfer ISDE Engineering Collaborative R&D, e.g. NRL/Vanderbilt NASA MSFC/Vanderbilt CREME-MC Site Major semiconductor supplier relationships NASA Center collaborative R&D Through students