Recent Progress on Emerging Transistor‐Based Neuromorphic Devices

Abstract

Figure 6. a) Characteristics of P-E loop for different switching states. b) A schematic diagram of the traditional FeFET structure, the applied gate voltage pulses, the switching states of multi-domain partial polarization, and the corresponding transfer curves, respectively. Reproduced with permission. [150]

Emerging polymer electrets for transistor-structured memory devices and artificial synapses

“…2b). 19 Channel conductance is regarded as the synaptic weight ( W ), whose changes are known as synaptic plasticity. In neurology, synaptic plasticity depends on the activity on either or both sides of a synapse.…”

“…The operation of ion-gate neuromorphic transistors is mainly divided into two types: electrostatic coupling and electrochemical doping/de-doping. 19 In ion-impermeable semiconductor materials, ions move in the electrolyte and accumulate at the electrolyte/gate electrode and electrolyte/channel layer interfaces under the action of an electric field. Owing to electrostatic coupling, carriers with opposite signs and equal charges can be induced on the channel layer and electrode side, forming a dense electric double layer at the interface (Fig.…”

4‐bit Multilevel Operation in Overshoot Suppressed Al2O3/TiOxResistive Random‐Access Memory Crossbar Array

“…In particular, to implement AI in an existing computing system, a huge amount of serial computation is required, and computation time increases sharply, which is a major limitation to the expansion of AI functions. [1][2][3][4][5][6][7][8][9][10] Computation time is more problematic when iterative weight sum calculations are required, such as in deep neural networks. [11][12][13] Therefore, it is necessary to create synaptic devices that can be connected to one neuron in a massively parallel manner as can be seen in a human neural network, and it must be able to implement a weighted sum for continuously incoming signals.…”

Synaptic transistors based on a tyrosine-rich peptide for neuromorphic computing

“…Neuromorphic devices have emerged to emulate human brain functions to overcome the von Neumann bottleneck. 2–6 In contrast to man-made computers, the biological brain is composed of ∼10 11 neurons and ∼10 15 synapses that process data locally and in parallel, thus affording extreme efficiency in terms of time and energy. 7 Synaptic devices that mimic the fundamental function of synapses have been proposed as building blocks for brain-like processors.…”