Ecofriendly Solution-Combustion-Processed Thin-Film Transistors for Synaptic Emulation and Neuromorphic Computing

Abstract

The ecofriendly combustion synthesis (ECS) and self-combustion synthesis (ESCS) have been successfully utilized to deposit high-k aluminum oxide (AlO x ) dielectrics at low temperatures and applied for aqueous In2O3 thin-film transistors (TFTs) accordingly. The ECS and ESCS processes facilitate the formation of high-quality dielectrics at lower temperatures compared to conventional methods based on an ethanol precursor, as confirmed by thermal analysis and chemical composition characterization. The aqueous In2O3 TFTs based on ECS and ESCS-AlO x show enhanced electrical characteristics and counterclockwise transfer-curve hysteresis. The memory-like counterclockwise behavior in the transfer curve modulated by the gate bias voltage is comparable to the signal modulation by the neurotransmitters. ECS and ESCS transistors are employed to perform synaptic emulation; various short-term and long-term memory functions are emulated with low operating voltages and high excitatory postsynaptic current levels. High stability and reproducibility are achieved within 240 pulses of long-term synaptic potentiation and depression. The synaptic emulation functions achieved in this work match the demand for artificial neural networks (ANN), and a multilayer perceptron (MLP) is developed using an ECS-AlO x synaptic transistor for image recognition. A superior recognition rate of over 90% is achieved based on ECS-AlO x synaptic transistors, which facilitates the implementation of the metal-oxide synaptic transistor for future neuromorphic computing via an ecofriendly route.

Synaptic transistor with tunable synaptic behavior based on a thermo-denatured polar polymer material

“…In the process of information transmission, neurotransmitters are transmitted through synapses so that previous neuron can be connected to other neurons. 52–56 This effective way of information transmission provides inspiration for the future development of artificial neural devices. Hence, implementing multiple synaptic responses in a single fixed device contributes towards the realization of a more efficient artificial neural network.…”

Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing

“…The internal conductance states of memristors depend on the history of the transport of electrons and/or the ions it has experienced, which can store information and simultaneously perform computation with the data stored. [ 225–235 ] The input bias voltages as a row vector is applied to the memristor‐based matrix in the 2D memristor crossbar array ( Figure a), in which Ohm's law and Kirchhoff's law are used for multiplication and addition, respectively. The accumulated current of the j column is, therefore, described as follows [ 236 ] where the I, V , and G stand for the current, bias voltage, and conductance, respectively.…”

Tunable Linearity of Weight Update in Low Voltage Synaptic Transistors with Periodic High‐k Laminates

“…[22] Tremendous attentions have been attracted on reducing the operation voltages without the sacrifice of nonvolatility, including the adopting of charge-trap-efficient floating gates, [23][24] high-k dielectrics (Al 2 O 3 , HfO 2 , ZrO 2 , etc.) [25][26][27][28] and high mobility channel layers (a-IGZO, IZO, etc.). [29][30][31][32] Alternative approaches have also been employed in device structures, such as double floating gate layers, in which the leaking of the trapped charge carriers could be prevented through the energy barrier of the floating gates.…”