Defense Advanced Research Projects AgencyTagged Content List

Microchips and Components

Relating to miniaturized electronic circuitry and its components and features

Showing 56 results for Microchips RSS
Over the past decade, DARPA’s investments in the advancement of Gallium Nitride (GaN) technology have helped enable the delivery of high power radio frequency (RF) signals at higher frequencies, bandwidths, and efficiencies. Today, however, a growing number of commercial and military components – from everyday smartphones to RF jammers – are generating a vast amount of RF signals, which is creating an increasingly crowded electromagnetic environment and a need to utilize higher operating frequencies – moving up to millimeter wave (mmW) frequencies
Next-generation intelligent systems supporting Department of Defense (DoD) applications like artificial intelligence, autonomous vehicles, shared spectrum communication, electronic warfare, and radar require processing efficiency that is orders of magnitude beyond what is available through current commercial electronics. Reaching the performance levels required by these DoD applications however will require developing highly complex system-on-chip (SoC) platforms that leverage the most advanced integrated circuit technologies.
Due to engineering limitations and cost constraints, the dynamics of the electronic industry are continually changing. Commercial companies increasingly recognize the need to differentiate their products through research in areas other than device scaling, such as new circuit architectures and computing algorithms.
The unrelenting progression of Moore's Law has created a steady cadence to ever-smaller transistors and more powerful chips, allowing billions of transistors to be integrated on a single system-on-chip (SoC). However, engineering productivity has not kept pace with Moore's Law, leading to prohibitive increases in development costs and team sizes for leading-edge SoC design. To help manage the complexity of SoC development, design reuse in the form of Intellectual Property (IP) modules has become the primary strategy.
Driven by the rapidly evolving national security threat landscape, future defense systems will need access to low size, weight, and power (SWaP) artificial intelligence (AI) solutions that can rapidly transition from idea to practice. In recent years, the ability to learn from large datasets has advanced significantly due to increases in hardware performance, advances in machine learning (ML) algorithms, and the availability of high quality open datasets.