Michael Hefenbrock

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About

Michael Hefenbrock is pioneering the convergence of neuromorphic computing and printed electronics, targeting the next generation of intelligent, flexible, and low-cost systems for soft robotics, wearables, and IoT devices. His research centers on designing and fabricating printed neuromorphic circuits—particularly those using inkjet-printed electrolyte-gated field-effect transistors (EGFETs)—to enable energy-efficient, biologically-inspired computation on unconventional substrates. Hefenbrock’s major contributions include the first realization and training of an inverter-based printed neuromorphic computing system (2021, 18 citations), a foundational step toward adaptive, on-demand printed AI. He also developed crossover-aware placement and routing algorithms specifically for inkjet-printed circuits (2020, 5 citations), addressing critical manufacturing constraints that previously limited circuit complexity. Most recently, he introduced an analog printed spiking neuromorphic circuit (2024, 5 citations), advancing the field toward ultra-low-power, event-driven computation in flexible form factors. His work bridges materials science, circuit design, and machine learning, offering a practical path toward truly embedded intelligence in soft, conformable, and disposable electronics. With a growing citation footprint and a clear trajectory from concept to implementation, Hefenbrock is establishing himself as a leading voice in printed neuromorphic hardware.