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Memory devices with sensitivity, selectivity, and operation voltage towards the gases are rarely reported for artificial olfactory sensors. Additionally, there are no reports available on the atomistic aspects of artificial olfactory sensors. This study reports an atomistic simulation of monoclinic‐ZrO2 (m‐ZrO2). The impact of external electric field on the formation of the oxygen vacancies are evaluated by considering the different directions of electric field. Furthermore, it is conducted nudged elastic band calculations which showed a decrease in the migration barrier energy with an increase in the electric field for all considered directions. Moreover, it is simulated the memristor device (Ta/m‐ZrO2/Pt) and investigated the impact of oxygen vacancies on electrical conductivity by considering oxygen vacancies at different locations in m‐ZrO2. Finally, it is evaluated the possibility of using the m‐ZrO2 based memristor device for an artificial olfactory sensor by studying the gas sensing properties of the (111) surface of m‐ZrO2. The pristine structure exhibits low sensitivity towards toxic molecules (CO2, CO, NH3, and NO2), while the sensing performance is significantly enhanced on the oxygen vacancy rich surface. These atomistic simulation results provide an atomic level understanding of the Ta/m‐ZrO2/Pt device and suggest the potential for it to be use as an artificial olfactory sensor. [ABSTRACT FROM AUTHOR]

Neuromorphic computers promise to enhance computing efficiency by eliminating conventional von Neumann architecture bottlenecks. Bio‐inspired artificial neural networks, such as feedforward neural networks and reservoir computing (RC), face challenges due to the unique memristor requirements. In this study, a dual‐gate ferroelectric polymer P(VDF–TrFE)‐coupled thin film transistor (DG–TFT) with an IGZO channel is presented. It yields complementary short‐ and long‐term memory functionalities are derived from the charge‐trapping/detrapping process at the IGZO‐SiO2 dielectric interface and ferroelectric polarization. These memory functionalities can be switched using different gated modes to meet the requirements of the reservoir and readout layers in RC. The bottom‐gated mode (BG‐mode) exhibits short‐term memory effects and nonlinear dynamics, whereas the top‐gated mode (TG‐mode) displays improved long‐term memory characteristics. To evaluate the long‐term memory properties, Python is used for pattern recognition. For the nonlinear dynamics and short‐term memory response of the BG‐mode, the DG–TFT is employed as a reservoir layer to handle various temporal tasks. Notably, the polarization level of the ferroelectric layer is coupled to improve the richness of the reservoir states, providing a reconfigurable RC system with an expanded capacity to effectively process and accommodate diverse signals. This holds potential for next‐generation hybrid intelligent applications. [ABSTRACT FROM AUTHOR]

For the progress of artificial neural networks, the imitation of multiple biological functions is indispensable for processing more tasks in a complex working environment. Memristors, which possess these advantages such as uniformity, high switching speed, and smaller device scale, are the better candidates compared to conventional complementary metal–oxide–semiconductor (CMOS) technology in artificial neural networks. In this work, an Ag/ZrOx/Pt threshold switching memristor (TSM) is designed to overcome the drawback of the large variation in the non‐volatile filament type memristor. The cycle‐to‐cycle and device‐to‐device variations are 5.6% and 4.9%. This device has mimicked the "nociceptive threshold," "relaxation," "no adaptation," and "sensitization" features for the nociceptor which can prevent the artificial intelligence system from dangers in the external environment. Additionally, with the change in the strength of the external stimulus, the artificial neuron is also built by emulating "all‐or‐nothing," "threshold‐driven‐spiking," and "strength‐modulated" characteristics. The proposed threshold‐switching memristor allows the simultaneous emulation of the biological nociceptor and leaky integrate‐and‐fire neuron for the first time, which represents an advance in the bioinspired technology adopted in future artificial neural networks and humanoid robots. [ABSTRACT FROM AUTHOR]

ZnO thin-film transistors (TFTs) using high-k hafnium oxide (HfO)x as the gate dielectrics achieve a low operating voltage of below 1 V. A negative threshold voltage shift of devices was observed after the negative bias illumination stress (NBIS) from a 635 nm laser. Plasma treatment of the HfOx dielectric surface using different gases (i.e., N2O and O2) was used to improve the TFTs' electrical stability. By calculating the subthreshold swing and analyzing the oxygen chemical bonding states nearby the ZnO/HfOx interface by x-ray photoelectron spectroscopy, we found that the interface trapped state and oxygen vacancies existed in ZnO active layer caused this unstable performance. The stability of ZnO TFTs is improved with using N2O plasma-treated HfOx dielectric, as this eliminate the defect in the ZnO layer. [ABSTRACT FROM AUTHOR]