SBIR-STTR Award

DMEA seeks to develop a robust technique to read DNA taggants from labeled electronic components to protect the electronic supply chain from counterfeiting. To address this need, Nanohmics Inc. proposes to develop an on-chip Sanger-based ion mobility sequencing technology (eSIM) with novel direct electrical read-out of the DNA sequence information. Specifically, the method involves incorporation of a novel porous semiconducting matrix that provides real-time, multiplexed, electrical readout of the diffusion of DNA fragments traversing chip-scale capillary channels. The method incorporates the high accuracy (99%) next-generation Sanger methods with a novel porous semiconducting matrix that provides real-time, multiplexed, electrical readout of the diffusion of DNA fragments and base sequence identity. The novel eSIM electrical read-out method overcomes one of the major barriers to the expanded utility of conventional dideoxy sequencing methods (e.g. microarrays and Sanger plates) - limitations imposed by fluorescence/optical-based labeling for endpoint detection.

Defense MicroElectronics Activity (DMEA) seeks to develop a robust technique to sample and read DNA taggants applied to surfaces of microelectronics supply chain components that employs machine learning methods to develop trained models capable of detecting counterfeit microelectronic parts with > 95% accuracy. To address the need for rapid (< 5 minute) genetic taggant signature profiling rooted in learned model inference vs. discretized, conventional genetic sequencing, Nanohmics Inc. proposes to develop an electronic model (eSIM) method based on a novel sensing array, with rapid DNA fingerprint signature determination. Specifically, the method involves incorporation of a novel matrix that provides real-time, multiplexed, electrical readout at the earliest stages of differential DNA migration across the axis of a chip-scale channel. Unlike traditional end-point optical read-out of fluorescently DNA, the eSIM platform aims to be significantly lower cost than current NGS systems because the read-out is compact and does not require a high-resolution optical subsystem. Machine Learning/AI algorithms will be employed to create a trained model for the signature, thus enabling detection of the DNA fingerprint from the model that employs techniques based on fragment length analysis, a well-established method used by the FBI in DNA forensics.