With an increasing demand for artificial intelligence, the emulation of the human brain in neuromorphic computing has led to an extraordinary result in not only simulating synaptic dynamics but also reducing complex circuitry systems and algorithms. In this work, an artificial electronic synaptic device based on a synthesized MoS₂ memristor array (4 × 4) is demonstrated; the device can emulate synaptic behavior with the simulation of deep neural network (DNN) learning. MoS₂ film is directly synthesized onto a patterned bottom electrode (Pt) with high crystallinity using sputtering and CVD. The proposed MoS₂ memristor exhibits excellent memory operations in terms of endurance (up to 500 sweep cycles) and retention (~ 10⁴) with a highly uniform memory performance of crossbar array (4 × 4) up to 16 memristors on a scalable level. Next, the proposed MoS₂ memristor is utilized as a synaptic device that demonstrates close linear and clear synaptic functions in terms of potentiation and depression. When providing consecutive multilevel pulses with a defined time width, long-term and short-term memory dynamics are obtained. In addition, an emulation of the artificial neural network of the presented synaptic device showed 98.55% recognition accuracy, which is 1% less than that of software-based neural network emulations. Thus, this work provides an enormous step toward a neural network with a high recognition accuracy rate.

Plant growth and development are negatively affected by a wide range of external stresses, including water deficits. Especially, plants generally reduce the stomatal aperture to decrease transpiration levels upon drought stress. Advanced technologies, such as wireless communications, the Internet of things (IoT), and smart sensors have been applied to practical smart farming and indoor planting systems to monitor plants’ signals effectively. In this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants. The stoma response, by which a plant adjusts to drought stress to maintain homeostasis, can be confirmed through the examination of evaporated water. Using a flexible PI-based sensor, a plant’s response variation is translated into an electrical signal. The sensors are integrated with a Bluetooth (BLE) module and a processing module and show potential as smart real-time water sensors in smart farms.