Ferroelectric Synaptic Transistor Network for Associative Memory

Brain-inspired associative memory is meaningful for pattern recognitions and image/speech processing. Here, a ferroelectric synaptic transistor network is proposed that is capable of associative learning and one-step recalling of a whole set of data from only partial information. The competition between an external field and the internal depolarization field governs the ferroelectric creep of domain walls and offers each single ferroelectric synapse a full and subfemtojoule-energy-cost Hebbian synaptic plasticity, including short-term memory (STM) to long-term memory (LTM) transition, and remarkably both spike-timing-dependent plasticity (STDP) and spike-rate-dependent plasticity (SRDP). Assisted by the third terminal to control the ferroelectric domain dynamics, self-adaptive coupling between neurons is realized by updating synaptic weight concurrently. Pavlov's dog experiment and multiassociative memories are demonstrated in this ferroelectric synaptic transistor network. Such ferroelectric synaptic transistor network is available for building multilayer neural networks and provides new avenues for associative-memory information processing.