Naveen Verma, Princeton University

Profile photo of Naveen Verma, expert at Princeton University

Associate Professor Princeton, New Jersey nverma@princeton.edu Office: (609) 258-1424

Bio/Research

Over the last four decades, the capabilities of integrated circuits (ICs) have expanded at an exponential rate according to Moore's Law. As a result, their application space has also expanded, from isolated server rooms to desktops to personal-area swarms to within the body. The pace has brought ...

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Bio/Research

Over the last four decades, the capabilities of integrated circuits (ICs) have expanded at an exponential rate according to Moore's Law. As a result, their application space has also expanded, from isolated server rooms to desktops to personal-area swarms to within the body. The pace has brought ICs to a point where they now face fundamental limits of energy, density, and performance. As a result, modern IC design requires new methods of scaling. Fortunately, since ICs have expanded into such a broad range of applications, there are many new opportunities to push their limits. These opportunities, however, are extremely diverse, requiring circuits that exploit the properties of new algorithms, new materials and devices, and new application characteristics. Our research focuses on analog and digital integrated circuits. Our emphasis is on developing system platforms for emerging applications, especially where considerable computation and instrumentation is required but energy is severely constrained. Important examples include implantable and wearable biomedical systems and remote sensing and processing network nodes. To drive this, there are two broad thrusts to our research: (1) application driven circuits and algorithms for ultra-low-power systems, and (2) platform components for low-power processing and communication in advanced and emerging technologies. By focusing on specific application domains, electronic systems can perform rich sensor acquisition and efficient algorithm computation. We aim to develop analog topologies to acquire diverse, multi-channel biomedical signals, and algorithm computation engines to extract specific correlations with physiological processes and conditions. Low-power processing and communication are critical means to reduce system power. To make their broad utilization increasingly viable, we aim to develop logic, connectivity circuits, and memory circuits (e.g., SRAM) to overcome emerging limitations in advanced technologies (e.g., nanometer CMOS) and alternate technologies (e.g., thin-film large-area electronics).

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