Block MEMS Wins $3.5M DARPA Contract to Protect Urban Environments Against Chemical Threats

Block MEMS, a leading developer of Quantum Cascade Laser (QCL) based infrared detection systems, has been awarded a $3.5M contract from the Defense Advanced Research Agency (DARPA), as part of the SIGMA+ Program. The goal of the program is to develop a persistent, real-time, early detection system for the full spectrum of chemical, biological, radiological, nuclear, and explosive (CBRNE) Weapons of Mass Destruction (WMD) threats at the city-to-region scale.

The SIGMA+ program builds on DARPA’s previous SIGMA program, which began in 2014 as an effort to significantly advance scalable detection capabilities to counter the threat of radiological and nuclear (RN) WMDs. SIGMA developed thousands of high-capability, low-cost detectors and networked them to demonstrate large-scale, continuously-streaming physical sensor networks.

The work will be done under a collaborative effort, led by SRI International, called Localization and Characterization of Chemical Anomalies in Urban Settings (LOCCUS). LOCCUS will combine SRI’s point sensing technology with Block’s standoff sensing technology to deliver an adaptive, high-sensitivity, ruggedized detection capability that exceeds chemical sensing capabilities today.

Dr. Anish Goyal, Block’s VP of Technology and principal investigator of the contract, commented, “The SIGMA+ contract extends Block’s standoff, chemical detection capabilities to the real-time, monitoring and mapping of urban environments. Block’s QCL technology and advanced algorithms are ideally suited for this application and we look forward to the development and, ultimately, deployment of such systems by operational partners.”

“This is a great opportunity to partner with DARPA and SRI International in the creation of technologies that promote global safety and security,” commented Al Weggeman, Block’s President.

Under this contract, Block MEMS will leverage its eye-safe laser capability to generate chemical detection maps via standoff sensing in a complex three-dimensional urban topography.