ABSTRACT
By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material conductance can be controlled by using an electrical input signal, which results in analog neuromorphic behavior. This paper proposes an ionic liquid (IL) multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL4 and H2O into the two ends of a discrete polydimethylsiloxane (PDMS) channel. The spike rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) behavior are highly stable by modulating the input signal. Furthermore, the discrete channel device presents highly durable performance under mechanical bending and stretching. Using the obtained parameters from the proposed ionic liquid-based synaptic device, convolutional neural network simulation runs to an image recognition task, reaching an accuracy of 84%. The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.
ABSTRACT
The human brain is the most efficient computational and intelligent system, and researchers are trying to mimic the human brain using solid-state materials. However, the use of solid-state materials has a limitation due to the movement of neurotransmitters. Hence, soft memory devices are receiving tremendous attention for smooth neurotransmission due to the ion concentration polarization mechanism. This paper proposes a core-shell soft ionic liquid (IL)-resistive memory device for electronic synapses using Cu/Ag@AgCl/Cu with multistate resistive behavior. The presence of the Ag@AgCl core shell in the liquid electrolyte significantly helps to control the movement of Cu2+ ions, which results in multistate resistive switching behavior. The core-shell IL soft memory device can open a gateway for electronic synapses.
ABSTRACT
This paper proposes a signal processed systematic 3 × 3 humidity sensor array with all range and highly linear humidity response based on different particles size composite inks and different interspaces of interdigital electrodes (IDEs). The fabricated sensors are patterned through a commercial inkjet printer and the composite of Methylene Blue and Graphene with three different particle sizes of bulk Graphene Flakes (BGF), Graphene Flakes (GF), and Graphene Quantum Dots (GQD), which are employed as an active layer using spin coating technique on three types of IDEs with different interspaces of 300, 200, and 100 µm. All range linear function (0-100% RH) is achieved by applying the linear combination method of nine sensors in the signal processing field, where weights for linear combination are required, which are estimated by the least square solution. The humidity sensing array shows a fast response time (Tres) of 0.2 s and recovery time (Trec) of 0.4 s. From the results, the proposed humidity sensor array opens a new gateway for a wide range of humidity sensing applications with a linear function.
