DoD STTR 2020.C

TECHNOLOGY AREA(S): Ground Sea, Weapons, Battlespace

OBJECTIVE:

Develop a solid propellant that greatly reduces the exhaust IR signature emitted while maintaining the thrust to mass ratio of the existing solid propellant.

DESCRIPTION:

This topic seeks to develop solid propellants that exhibit reduced IR signatures while maintaining thrust to mass ratio performance. Detection of missile launch and booster burnout are important threat identification points. Since remote IR surveillance is often used to detect and track missile launches, the ability to avoid detection through IR signature reduction would be beneficial for mobile defense platforms as well as forward deployed offensive assets.

PHASE I:

Develop a proof of concept solid rocket motor propellant that greatly reduces the exhaust IR signature. Perform an analysis to demonstrate the concept and an initial understanding of the signature calculations while maintaining the thrust to mass ratio. Phase I should be a feasibility concept study that supports the proposed design solution and down selection of alternatives.

PHASE II:

Enhance and refine the proposed propellant based on the results and findings of Phase I and expand its capabilities. Validate the feasibility of the Phase I concept by development and demonstrations that will be tested to ensure performance objectives are met. The Phase II effort should result in a prototype with substantial commercialization potential.

PHASE III:

Productize the propellant to expand the capabilities to other interested users. Develop and execute a Phase III incremental test & integration plan that produces a final prototype.

KEYWORDS: Solid Propellant, Reduced IR Signature, maintain thrust to mass ratio

References:

1. M. Keith Hudson, Robert B. Shanks, Dallas H. Snider, Diana M. Lindquist, Chris Luchini, and Sterling Rooke, UV, Visible, and Infrared Spectral Emissions in Hybrid Rocket Plumes, Department of Applied Science, Univ of Ark at Little Rock.

2. Advisory Group for Aerospace Research & Development, Advisory Report 287, Terminology and Assessment Methods of Solid Propellant Rocket Exhaust Signatures, February 1993.

3. Sam Judd, Matthew Vernacchia, Solid Rocket Propellant Combustion, Massachusetts Institute of Technology.

4. R.C. Farmer, S.D. Smith, B.L. Myruski,Radiation from Advanced Solid Rocket Motor Plumes, SECA-FR-94-18, NASA.

TECHNOLOGY AREA(S): Electronics, Ground Sea, Sensors

OBJECTIVE:

Develop innovative designs for a bio-inspired sensor that is optimized for autonomously detecting, identifying, tracking, and reporting dim missile threats in cluttered and noisy scenes.

DESCRIPTION:

This topic seeks innovative solutions for autonomously (i.e. without a cue from another sensor) detecting dim missile threats in cluttered and noisy scenes using passive sensors. An example application could be detection of a distant (e.g. 100 kilometers away) re-entering missile using a ground-based infrared search and track sensor. In addition to the background and sensor noise, the scene might be cluttered by moving sources to include (but not limited to) clouds, dust, precipitation, weapon effects, the sun, the moon, stars, meteors, satellite flares, auroras, birds, insects, and aircraft. Such a scene could be challenging for conventional detection approaches, and would require increased size, weight, and power (SWaP) in order to reject noise and clutter while increasing target sensitivity.

Biological vision systems are SWaP-efficient and well adapted for ignoring clutter and noise, detecting motion, and compressing visual information. A sensor that artificially emulates all or part of a biological vision system might outperform conventional sensors for detecting, identifying, tracking, and reporting dim missile threats in cluttered and noisy scenes.

This topic seeks innovative sensor designs that artificially mimic biological vision systems wherever feasible and are capable of overcoming the challenges described above. Offerors should propose complete designs, to include everything from the optics taking in the scene to the final processor outputting target reports. These designs should incorporate technologies that are projected to mature (preferably driven by commercial investments) within the next 10 years, and that would be available (as early prototypes) for experiments during Phase II.

The focus of this topic is not on the development of any one particular technology but rather the integration of multiple emerging technologies into a novel solution. The Research Institute partner should be a key member of the design team and a source of many of the innovative ideas, rather than supplying one or two services or subcomponents. Offerors may use the example application described above or propose their own notional application and corresponding sensor configuration (e.g. waveband, field-of-view, etc.) as long as its feasibility and suitability for missile defense applications can be established.

In addition to performance, there are other considerations that determine the acceptability of a sensor concept for deployment. These considerations include manufacturability, ease-of-calibration, ability to handle multiple simultaneous targets, minimization of (or compensation for) non-linearities and non-uniformities, insensitivity to (or compensation for) vibration and temperature changes, hardening against radiation and EMP, ability to be programmed and trained, and system support requirements (e.g. cooling, data-link, and off-board processing requirements). The bio-inspired sensor design should address these considerations.

PHASE I:

Develop an initial design for a bio-inspired sensor. Study the scientific and technical feasibility of the proposed approach. Estimate its performance using low-fidelity calculations, models, and simulations. Develop an initial plan for fabricating a prototype in Phase III. Assess the availability and maturity of enabling technologies and subcomponents within the next 5-10 years based on market projections. Identify risk areas and mitigation plans that would be implemented in Phase II. Complete a plan for Phase II and contact suppliers to verify the plan is executable.

PHASE II:

Conduct integration, risk-reduction, and proof-of-concept experiments using early prototype subcomponents and subassemblies in order to inform models and increase confidence in the feasibility and benefit of the proposed design. Improve the design based on these experimental results. Conduct medium-fidelity calculations, models, and simulations to estimate sensor performance, behavior, and support requirements. Complete a detailed plan for fabricating a prototype in Phase III.

PHASE III:

Fabricate and test a complete bench-top prototype of the bio-inspired sensor. Identify design modifications that could be made to serve other customers and applications. Complete plans for a transportable, ruggedized, and miniaturized prototype that could be field-tested.

KEYWORDS: Bio-inspired, Missile Defense, Sensor

References:

1. J. H. Pantho, P. Bhowmik and C. Bobda, "Neuromorphic Image Sensor Design with Region-Aware Processing," 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Miami, FL, USA, 2019, pp. 459-464.

2. M. A. Massie et. al. Neuromorphic infrared focal plane performs sensor fusion on-plane local-contrast-enhancement spatial and temporal filtering, Proc. SPIE 1961, Visual Information Processing II, 27 August 1993.

3. K. I. Schultz et. al. Digital-Pixel Focal Plane Array Technology, MIT Lincoln Laboratory Journal, Vol. 20, No. 2, December 2014.

4. G. P. Luke, C. H. G. Wright and S. F. Barrett, "A Multiaperture Bioinspired Sensor With Hyperacuity," in IEEE Sensors Journal, vol. 12, no. 2, pp. 308-314, Feb. 2012.

5. D. Scribner, T. Petty and P. Mui, "Neuromorphic readout integrated circuits and related spike-based image processing," 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, 2017, pp. 1-4.

TECHNOLOGY AREA(S): Electronics, Sensors, Space Platforms

OBJECTIVE:

Develop radiation hardened electronic components capable of surviving and operating through exposure to radiation environments encountered in space.

DESCRIPTION:

This topic seeks the design and fabrication of inherently radiation hardened microelectronic components. Electronic components and systems exposed to radiation in space may experience power resets, safing (de-arming), performance degradation, and/or temporary or permanent failure due to cumulative effects of long-term exposure or high energetic particle and/or photon fluence. Radiation sources in space include particles geo-magnetically confined in radiation belts (protons, electrons, heavy ions); particles from solar winds, coronal mass ejections (proton rich) or flares (heavy ion rich); omnidirectional free space particles (galactic cosmic rays, heavy ions); or particles and photons from man-made events (X-rays, Gamma-rays, neutrons, radioactive debris) as well as electro-magnetic pulse (EMP). Typically, systems employ a combination of methods for radiation protection: shielding, part redundancy, circumvent and recovery (C&R), rad-hard by design (RHD), and hardened parts. Using shielding and redundant parts imposes mass penalties. C&R places a system in a protective mode until a radiation event passes leaving the system vulnerable during the down time. RHD develops radiation tolerant circuits that minimize single point failures. The hardened parts approach involves design, fabrication, selection and screening of parts for radiation tolerance.

New manufacturing techniques and recent developments in nano-materials create an opportunity to develop electronic components that are inherently insensitive to radiation effects. In particular, vacuum field effect component technology (e.g. diodes, triodes, transistors) and functional devices made from these components (e.g. OPAMPs, simple logic devices) using high density three dimensional (3D) radiation hardened capability requiring minimal shielding and/or C&R.

Desire parts able to survive and operate through space radiation environments with recommended total ionizing dose (TID) >300 krads (Si), single event upsets (SEU) < 10-10 errors/bit-day, and immunity to single event latch-up (SEL) at linear energy transfer (LET) levels > 100 MeV cm2/mg. Development of a radiation hardened field-programmable gate array, with a technology node less than 45nm, is a specific government application for this technology.

PHASE I:

Design radiation insensitive component(s), simple circuit(s), and/or 3D fabrication technique(s). Provide analysis substantiating proposed component(s), simple circuit(s), and/or 3D fabrication technique(s) can survive and operate through realistic radiation environments. Fabricate simple proof of principle prototypes and establish baseline performance parameters.

PHASE II:

Optimize design(s) to improve baseline performance, increase survivability and level of operability in realistic radiation environments. Fabricate and test optimized parts in realistic radiation environments and against standard military temperature cycling specification. Work with a vendor/trusted foundry/fabrication house and/or military prime contractor on part(s) manufacturability/producibility. Incorporate hardened parts in a representative space avionic subsystem/system application and test in realistic space radiation environments.

PHASE III:

Team with a vendor/trusted foundry/fabrication house and/or military prime contractor to develop and space qualify radiation-hardened parts. Work with the transition partner to establish a pathway to insert technology into an existing or planned missile defense application.

KEYWORDS: Vacuum, Channel, Tube, Nanotechnology, Nanomaterials, Microelectronics, Transistor, Radiation, Hardening

References:

1. Demming, A., Vacuum technology comeback immunizes nanoelectronics from radiation, Physics World, IOP Publishing, 31 Aug 2013. https://physicsworld.com/a/vacuum-technology-comeback-immunizes-nanoelectronics-from-radiation

2. Markoff, J., Smaller Chips May Depend on Vacuum Tube Technology, The New York Times, 5 Jun 2016. https://www.nytimes.com/2016/06/06/technology/smaller-chips-may-depend-on-technology-from-grandmas-radio.html

3. Han, J. and Meyyappan, M., Introducing the Vacuum Transistor: A Device Made of Nothing, IEEE Spectrum, 23 Jun 2014. https://spectrum.ieee.org/semiconductors/devices/introducing-the-vacuum-transistor-a-device-made-of-nothing

4. Srisonphan, S., Jung, Y. & Kim, H. Metal-oxide-semiconductor field-effect transistor with a vacuum channel. Nature Nanotech 7, 504-508 (2012). https://www.nature.com/articles/nnano.2012.107

TECHNOLOGY AREA(S): Electronics, Sensors, Space Platforms

OBJECTIVE:

Develop a new capability that transforms low energy accelerators to high energy accelerators or develops a brand new accelerator specifically designed for high energy heavy ion testing of electronics.

DESCRIPTION:

This topic seeks a flexible testing facility capable of delivering high energy beams which can test electronics in a representative configuration and reduce the overall testing cost while fully characterizing the Single Event Effects (SEE) response of each part. The United States and its military are sending more, and increasingly complex, computer-run devices into orbit each year. Once in orbit, the circuits within these devices are bombarded by ionizing radiation that can lead to failure. Given the increasing expense of launching space based systems and the microelectronics which reside in them, the testing of these integrated circuits at heavy-ion beam facilities is essential to prevent costly losses due to radiation failure.

The increasing complexity of electronic circuits, with smaller feature sizes and larger overlayers, has made it harder to test at ion beam facilities as the circuits require expensive and difficult preparation for the low-energy ion beams currently in use. In space, high energy ionizing particles can easily traverse the overlayers to reach the sensitive volume where SEE occur. Accelerator facilities performing SEE testing use lower-energy ion beams, which have difficulty reaching these sensitive volumes. Therefore, costly de-lidding of parts is required which is a destructive process removing the outermost layers of a circuit and leaving the exposed circuit in a state that can be difficult to test (e.g. thermal properties are altered) and which is not representative of the on-orbit configuration of the circuit.

PHASE I:

Develop a concept to improve existing low energy test capabilities (10 MeV/n or less ion accelerators) and increase their energy to 100 MeV/n or more. Or develop a concept to create a new accelerator that reaches 100 MeV/n or more and can fit into a standard shipping container. Standard ISO shipping container dimensions are: 8ft (2.43m) wide, 8.5ft (2.59m) high and 40ft (6.06m) (Threshold) or 20ft (12.2m) (Objective) long. Provide a detailed report documenting the concept design and its expected max energy levels. For new designs, provide a phased plan of the critical elements to be prototyped if the entire design cannot be prototyped in one follow-on phase.

PHASE II:

For designs enhancing current accelerators: Create and provide a prototype of the improved elements/subcomponents for upgrading or adapting a current ion accelerator design to reach the enhanced energy level documented in Phase I. Provide modeling and simulation to demonstrate a complete final design along with a documented approach for implementing these elements and enhancements on a current accelerator design. Identify potential accelerator facilities or manufacturers with which to partner for Phase III implementation.

For new designs: Create and provide a prototype of the new design. If the full prototype cannot be completed in this phase, create and provide prototypes of the critical parts/subcomponents of a new design that would be essential for meeting the increased energy benchmark of 100 MeV/n or more along with modeling and simulation of the final design to demonstrate the capability to fit into a standard shipping container.

PHASE III:

Build an operational, improved, or new design ion accelerator that can reach 100 MeV/n or more and operate for 2,000 hours per year. For a design that is an improvement on existing accelerators, if necessary, partner with existing ion testing facilities and/or a manufacturer of current accelerators to demonstrate implementation of the improved design. For new designs, build the final accelerator to fit within a standard shipping container.

KEYWORDS: Ionizing radiation, testing, SEE, Heavy-ion, accelerator, microelectronics

References:

1. Pellish, Jonathan A. et al., Heavy ion testing at the galactic cosmic ray energy peak, IEEE - 2009 European Conference on Radiation and Its Effects on Components and Systems (RADECS), Sept. 14-18, 2009.

2. Schwank, James R. et al, Radiation Hardness Assurance Testing of Microelectronic Devices and Integrated Circuits: Test Guideline for Proton and Heavy Ion Single-Event Effects, IEEE-Transactions on Nuclear Science, Vol. 60, No. 3, June 2013.

TECHNOLOGY AREA(S): Electronics, Sensors, Space Platforms

OBJECTIVE:

Develop RHBD products and Intellectual Property (IP) based on on-shore 22nm FinFET technology to meet long term performance and availability needs for defense applications in natural and hostile radiation environments.

DESCRIPTION:

This topic seeks to leverage the current 22nm on-shore production capability and its inherent Total Ionizing Dose (TID) hardness by:

1. Developing RHBD mitigation approaches to known susceptibilities to low energy particle exposure to allow for long term design solutions across platforms.
2. Developing U.S. based IP for the RHBD designs which allows for easy modification by various government programs depending on the intended application.
3. Conducting additional hardening and testing of the RHBD 22nm FinFET technology for performance in hostile environments to provide an even greater depth of its use across platforms and applications.

It is critical to the development and sustainment of defense programs to identify, invest in, and advance secure, on-shore manufacturing and packaging of RHBD technology and IP, including characterizing the technology in radiation environments. 22nm FinFET technology is a proven commercial technology with current on-shore production, allowing for advanced size, weight, and power considerations in new designs.

PHASE I:

Design radiation insensitive component(s), simple circuit(s), and/or 3D fabrication technique(s) using 22nm FinFET technology. Provide analysis substantiating proposed component(s), simple circuit(s), and/or 3D fabrication technique(s) can survive and operate through realistic radiation environments (both natural space and weapon induced). Fabricate simple proof of principle prototypes and establish baseline performance parameters. Conduct initial operational and evaluation testing in prompt dose-rate radiation environments. Characterize survivability and operability in realistic natural space and prompt dose rate radiation environments, and against standard military temperature cycling specification environments.

PHASE II:

Optimize design(s) to improve baseline performance and increase survivability and level of operability in realistic natural space and weapon-induced radiation environments. Fabricate and test optimized parts in realistic natural space and prompt dose rate radiation environments and against standard military temperature cycling specification environments. Work with a vendor, trusted foundry, fabrication house, and/or military prime contractor on part(s) manufacturability and producibility. Incorporate hardened parts in a representative space avionic subsystem/system application and test in a realistic space radiation environment.

PHASE III:

Team with a vendor, trusted foundry, fabrication house, and/or military prime contractor to develop and space qualify the radiation hardened parts. Work with the transition partner to establish a pathway to inserting the technology into an existing or planned missile defense application.

KEYWORDS: Radiation, RHDB, 22nm, microelectronics, state-of-the-art, foundry, on-shore, defense, sensors

References:

1. Lee, H.J et al., Intel 22nm FinFET (22FFL) Process Technology for RF and mm Wave Applications and Circuit Design Optimization for FinFET Technology, IEEE-2018 IEEE International Electron Devices Meeting (IEDM).

2. Guillorn, M. et al., FinFET performance advantage at 22nm: An AC perspective, Guillorn, M. et al., IEEE-2008 Symposium on VLSI Technology.

3. Royer, Pablo et al., Evolution of radiation-induced soft errors in FinFET SRAMs under process variations beyond 22nm, IEEE-Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH'15).

4. Sanjana S.R. et al., Design and Performance Analysis of 6T SRAM Cell in 22nm CMOS and FINFET Technology Nodes, IEEE-2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT).Approved for Public Release 20-MDA-10521 (2 Jul 20)

TECHNOLOGY AREA(S): Information Systems

OBJECTIVE:

Develop novel techniques and metrics for evaluating machine learning -based computer vision algorithms with few examples of labeled overhead imagery.

DESCRIPTION:

The National Geospatial Intelligence Agency (NGA) produces timely, accurate and actionable geospatial intelligence (GEOINT) to support U.S. national security. To exploit the growing volume and diversity of data, NGA is seeking a solution to evaluate the performance of a class of algorithms for which there are a limited quantities of training data and evaluation data samples. This is important because statistical significance of the evaluation results is directly tied to the size of the evaluation dataset. While significant effort has been put forth to train algorithms with low sample sizes of labelled data [1-2], open questions remain for the best representative evaluation techniques under the same constraint.

Of specific interest to this solicitation are innovative approaches to rapid evaluation of computer vision algorithms at scale, using small quantities of labelled data samples, and promoting extrapolation to larger data populations. The central challenge to be addressed is the evaluation of performance with the proper range and dimension of data characteristics, when the labeled data represents a small portion of the potential operating conditions. An example is when performance must be evaluated as a function of different lighting conditions, but most of the labelled data was collected under full sun.

The study will be based on panchromatic electro-optical (EO) imagery using a subset (selected by the STTR participants) of the xView detection dataset, although extension to other sensing modalities is encouraged. Solutions with a mathematical basis are desired.

PHASE I:

Develop and demonstrate methods and metrics to evaluate machine learning -based computer vision algorithm performance with low sample sizes of labeled EO imagery. The characteristics of the selected data subset should include variation across at least two operating conditions, such as (for example) geographic diversity and object size . Offerors should state those characteristics that will vary in their selected dataset. Offerors should detail anticipated challenges associated with this problem, and how to address those challenges, together with methods to provide uncertainty estimates for assessment results. Phase I will result in proof-of-concept performance assessment on the selected dataset. Phase I will deliver all data collected or curated, and a final report that contains: description of technical approach, assessment results, and identify methods to extend to different data sources and conditions.

PHASE II:

Develop refinements to address identified deficiencies from Phase I. Extend Phase I capabilities through application to video, infrared, or multi-spectral sensor imagery, and demonstrate against an operational dataset for both EO panchromatic imagery and the additional sensing type(s). Extend the Phase I dataset to include more sparsely represented data characteristics, which also include additional variation. Deliverables include assessment results and code.

PHASE III:

Virtually all domains face an issue of lack of labeled data so better prediction and understanding the likely performance and potential range of that performance, given few examples for empirical performance evaluation, will have wide ranging military and commercial applications. Military applications include assessing algorithms for automated tracking, search and rescue, and hazardous target detection; commercial applications also include tracking, search and rescue, and agriculture.

KEYWORDS: Performance Evaluation; Algorithm Assessment; Low Sample Size; Machine Learning; Deep Learning; Few Shot Learning; Unsupervised Learning

References:

1. L. Fei-Fei, R. Fergus and P. Perona. "One-Shot learning of object categories." IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594 - 611, 2006.

2. W. Wang, et al. "A Survey of Zero-Shot Learning: Settings, Methods, and Applications." ACM Transactions on Intelligent Systems and Technology, 10(2), article 13, 2019.

TECHNOLOGY AREA(S): Information Systems

OBJECTIVE:

Resolve restrictions of the Linux Kernel Virtual Machine (KVM) to enable control of the time stamp counter to keep software execution rate consistent with non real-time simulation time.

DESCRIPTION:

This topic seeks to develop an accurate timing source for software execution on a KVM to enable non real-time simulations. The Linux KVM supports the use of physical computer cores to accelerate the execution of virtual machines. In this paradigm, peripheral hardware is simulated in a user space (ie. non-privileged space) process (ie. the allocation of memory and time) while streams of machine instructions to be executed by the virtual cores are supplied to the KVM module of the operating system, which executes those streams on real hardware. Timing accuracy is not as much of a concern as control of the time stamp counter for non real-time simulation time control instead of its intended virtualization use. Several aspects of this virtualization method prove challenging when the streams of machine instructions are intended to be executed as part of a simulation model rather than a virtualization tool. Among the most challenging are (1) determining how the KVM populates the time stamp counter register of the virtual core, and (2) accounting for the number of instructions to be executed by the KVM between returns to user space which cannot be controlled directly by the user space process. This impedes the use of the time stamp counter as a timing source because it cannot be matched to a non real-time simulation and it prevents the presentation of a consistent rate of execution to the software hosted on the virtual machine for use in non real-time simulations.

PHASE I:

Develop the proposed approach to a sufficient level to demonstrate its viability and identify requirements for full development. The following are anticipated at the conclusion of Phase I: a) A demonstration/proof-of-concept of the viability of the proposed approach. b) An algorithmic/process description of the developed approach, to include use-case descriptions and descriptions or demonstrations of output. c) A plan for the development of an initial working prototype capability, to include cyber security efforts to gain approval to operate on government computers.

PHASE II:

Deliver an initial working prototype capability. The following are anticipated by the conclusion of Phase II: a) A demonstration of initial capability. b) Prototype software for experimental trials by government users. c) Documentation, including software scan results, to support approval decisions to load software onto government computer systems. d) Documentation of the initial capability sufficient to support trial use. e) Documentation of the software architecture, its algorithms/processes, and output formats. f) A plan for development of a full operational capability.

PHASE III:

Deliver phased incremental improvements to the prototype until a full operational capability is achieved. At each increment, the following are anticipated: a) Demonstrations of incremental additions/improvements. b) A software release for use/testing by the government. c) Documentation, including software scan results, to support approval decisions to load software onto government computer systems. d) Updated user documents. e) Updated architecture, algorithms/processes, and output format documentation. At the conclusion of Phase III, the final software and documentation should be delivered.

KEYWORDS: Simulation Time Management, KVM, non real-time, emulation

References:

1. Zachary Amsden. Timekeeping Virtualization for X86-Based Architectures. Accessed 14 April 2020: https://www.kernel.org/doc/Documentation/virtual/kvm/timekeeping.txt.

2. T. Yeh and M. Chiang, "On the interface between QEMU and SystemC for hardware modeling," 2010 International Symposium on Next Generation Electronics, Kaohsiung, 2010, pp. 73-76.

3. Ozmen, Ozgur, Nutaro, James J., Sanyal, Jibonananda, and Olama, Mohammed M. Simulation-based Testing of Control Software. United States: N. p., 2017. Web. doi:10.2172/1343541.