OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software; Microelectronics The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: To develop a software that combines images captured using different modalities (e.g., X-Ray, Terahertz Imaging, etc.) and fuses those images together in order to produce a 3D volume (solid model) of a circuit card assembly (CCA) and all of its attendant surface and subsurface layers devoid of image artifacts such as shadowing, beam hardening, and cross-surface saturation. DESCRIPTION: USAF electronic systems and subsystems eventually require maintenance work to ensure the continued functionality of aging avionics weapon systems and test equipment. In many cases, technical data and/or support from the original manufacturers may not exist. To ensure proper maintenance of the equipment in these cases, avionics systems and test equipment need to be reverse engineered. The reverse engineering process includes analyzing, measuring, and testing avionics systems down to the component level to determine component functionality and identify necessary repair requirements. Circuit card assemblies (CCAs) are one of the most commonly reverse engineered microelectronics in support of maintaining legacy avionics systems. CCAs are custom-designed to complete computations, measurement, and control avionics systems and comprise multi-layer printed circuit boards (PCBs) and microelectronic components. During CCA reverse engineering process, multiple imaging modalities can be utilized to produce images of all layers (surface and subsurface) of a CCA. These images capture all components on a board, as well as internal traces, to inform future reverse engineering requirements for these legacy systems. The goal of this image capture process is to capture an image of each board layer devoid of noise and other artifacts such as X-Ray beam hardening. As part of the image capture, the images are merged to produce a 3D volume of a CCA capturing surface and subsurface layers, and each of the stacked layers of the CCA need to be captured in the volume. In some cases, components from the CCAs cannot be removed, or depopulated during the image capture process, in order to maintain the functionality of the CCA under analysis. CCA images are generally captured using X-Ray CT system, but other image modalities such as Terahertz Imaging (THz) or optical imagery can be used to capture the CCA and its components if X-Ray CT is deemed insufficient for image capture (e.g., materials on a CCA that are too dense to be penetrated by X-Ray). In cases where multiple image modalities are used to capture a CCA, the resulting images need to be merged to recover the design of the CCA for subsequent reverse engineering analysis. Thusly, a software that combines and fuses images captured using different modalities into solid models would greatly enhance reverse engineering operations. PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Air Force expects the applicant(s) to demonstrate feasibility by means of a prior Phase I-type effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. The applicant(s) shall demonstrate the present capability to automatically fuse different imaging modalities in order to correct for noise and artifact generation. The demonstrated capability should expand upon related multiple image modalities such as X-Ray, Terahertz, Optical, and any other frequently used imaging modalities . The baseline capability in this situation should reflect the ability to combine two image modalities, one of which must be X-Ray. As context, the related environment here is pertinent for situations where fusing images, assets under analysis cannot be depopulated (if applicable) and images must be captured using nondestructive methods. PHASE II: Phase II should focus on developing a tool that combines imagery data from different imaging modalities and corrects for artifact generation (e.g., shadowing, beam hardening, etc.). A successful tool will produce a fused image that is as devoid as possible of any artifacts from any of the component images. The tool should also fuse at least two different image modalities to extend the merging capability identified in Phase I. Additionally, the tool should conduct an intelligent merge that accentuates component features captured in different modalities. PHASE III DUAL USE APPLICATIONS: Wherever Phase II produces a tool that is capable of fusing images from different imaging modalities and successfully correcting for noise and artifact generation, the awardee will be expected to support the Air Force in transitioning the technology for wider US Government use. At this point in development, the tool should automatically fuse images between multiple modalities and correct for image noise such as shadowing, blurring, beam hardening, and other image artifacts. Working with the Air Force, the company will integrate the technology into the operationally relevant environment for use on mission projects. The results of the integration and subsequent evaluation/analysis of the deployed tool will inform future requirements and development phases (if applicable). REFERENCES: 1. Bieszczad, Jerry & Ueckermann, Mattheus. (2023). 3-D Reconstruction, Visualization, and Modeling of Buildings from Multiple Image Sources. Creare LLC. 2. Geeta, Mary & Shrida Kalamkar (2023). Multimodal Image Fusion: A Systematic Review. Decision Analytics Journal. https://www.sciencedirect.com/science/article/pii/S2772662223001674 3. Archambault, Louis & Fatemi, Ali & Safari, Mojtaba. MedFusionGAN: Multimodal Medical Image Fusion Using an Unsupervised Deep Generative Adversarial Network. BMC Medical Imaging. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-023-01160-w KEYWORDS: Image Fusion; X-Ray; Terahertz; Image Capture; Scanning Electron Microscope; Optical Imagery; Reverse Engineering; Circuit Card Assembly; Printed Circuit Board
