Utilizing electronic, ionic and photonic coupling for neuromorphic devices - John, Rohit Abraham (Nanyang Technological University, 2019)
Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed memory and parallel processing capability has recently gained more traction. Memristive devices emulating brain-like signal processing forms the foundation for development of efficient learning circuitry, but few devices offer analog-like switching transitions, symmetry, linearity and wide range of dynamic conductance states, necessary for efficient computation. Conventional drift-based memristors relying on stochastic metallic/defect filament formation fall behind in this regard due to their abrupt switching transitions. Very recently, diffusive memristors and second-order drift memristors have been engineered to approximate the biological synaptic dynamics based on metal atom diffusion, thermal dissipation, mobility decay and spontaneous nanoparticle formation. A common feature in all these systems is the insertion/extraction of mobile ions, directly or indirectly altering the electronic conductivity in a continuous analog-like manner. Hence, a hybrid ionic-electronic (ionotronic) conduction mechanism could be considered vital to mimic the biological neural dynamics. This calls for the need of material systems and device configurations where ionic and electronic conductivities co-exists intimately and which could be synergistically controlled in a manner, temporally similar to neural dynamics.