The performance of current information processors are predominantly based on complementary metal-oxide-semiconductor (CMOS) transistors. However, CMOS scaling have started to face significant challenges and besides the physical limits, the conventional computing paradigm based on binary logic and Von Neumann architecture is becoming increasingly inefficient with onset of big data revolution and growing complexity of computation. Neuromorphic computing is the state-of-the-art research trend in the field of memory and logic devices where the goal is to build a versatile computer that is efficient in terms of energy and space, homogeneously scalable to large networks of neurons and synapses, and flexible enough to run complex behavioral models of the neocortex as well as networks inspired by neural architectures. Memristors, with their gradually modified conductivity level can mimic the biological synapses. Low energy consumption, ultrafast operation and small dimensions are the most essential requirements for a memristor to perform tasks similar to a synapse and become as efficient as human brain. A ferroelectric tunnel junction (FTJ), where gradual modulation of conductance can be achieved by controlled rotation of ferroelectric domains can act very efficiently as a synapse. Also the non-volatility of the stored information in the ferroelectric memories make them even more attractive as potential candidates for future neuromorphic computing building blocks. Here, we report on the performance of FTJs with a spin-coated organic ferroelectric P(VDF-TrFE) tunnel barrier. We have measured up to 107% tunneling electroresistance (TER) effect in these FTJs on a semiconducting Nb-doped STO bottom electrode at room temperature that persists until the ferroelectric Curie point of P(VDF-TrFE) . Also these junctions show very clear and reproducible memristive behavior based on variable amplitude and duration of the applied voltage pulses, fast switching, long data retention of the high, low and different intermediate states, which is extremely promising for neuromorphic applications.
 S. Majumdar, B. Chen, Q. Qin, H. S. Majumdar, S. van Dijken, Adv. Func. Mater. (in press).