The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

A comprehensive review on emerging artificial neuromorphic devices

Detection and quantification of hydrogen sulfide (H2S) gas is important as it influences directly human health, our environment, and operations of several industries including food and beverages, oil, construction, and medicine. It also acts as biomarker in diagnosis of halitosis at early stage. We report herein liquid exfoliated MoSe2 nanoflakes based stable chemiresistive H2S gas sensor which operate at moderate temperature of 200℃. The response of p-type MoSe2 gas sensor device (when operated in ambient environment) was found to be varying between 15.87%–53.04% when the concentration of H2S was varied between 50 ppb – 5.45 ppm. The response of the device decreases when the measurements were done in synthetic air environment and it varies between 7.13%–19.87% for the concentration range of 500 ppb - 5.45 ppm. The response (%), recovery rate (%), hysteresis, experimental lowest detection limit etc. of the device suggest that the device performs better when operated in ambient than in synthetic air which suggest its real time device application. The response time and recovery time of the sensor are 15 s and 43 s respectively for 100 ppb of H2S. The sensor performance was found to be highly repeatable with sensitivity of 5.57%/ppm of H2S. The theoretical limit of detection and limit of quantization of the device were found to be 6.73 ppb and 22.44 ppb respectively. Based on chemical analysis, a plausible mechanism based on charge transfer phenomenon has been proposed for this sensor.

/pdf/mose2-nanoflakes-based-chemiresistive-sensors-for-ppb-level-3qn1n2ubih.pdf

MoSe2 nanoflakes based chemiresistive sensors for ppb-level hydrogen sulfide gas detection

1D Metal Oxide Semiconductor Materials for Chemiresistive Gas Sensors: A Review

New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay

As a typical n-type semiconductor, MoO3 has been widely applied in the gas-detection field due to its competitive physicochemical properties and ecofriendly characteristics. Volatile organic compounds (VOCs) are harmful to the atmospheric environment and human life, so it is necessary to quickly identify the presence of VOCs in the air. This review briefly introduced the application progress of an MoO3-based sensor in VOCs detection. We mainly emphasized the optimization strategies of a high performance MoO3, which consists of morphology-controlled synthesis and electronic properties functional modification. Besides the general synthesis methods, its gas-sensing properties and mechanism were briefly discussed. In conclusion, the application status of MoO3 in gas-sensing and the challenges still to be solved were summarized.

/pdf/volatile-organic-compounds-gas-sensors-based-on-molybdenum-2uso1tk723.pdf

Volatile Organic Compounds Gas Sensors Based on Molybdenum Oxides: A Mini Review.

We report dual ion beam sputtering fabrication of an Al/ZnO/Al memristor displaying forming-free bipolar resistive switching characteristics with memristive behavior without necessitating any post-processing steps. A nearly amorphous ZnO thin film and an appropriate concentration of oxygen vacancies play a significant role in imparting forming-free, stable, and reliable behavior to memory cells. Besides, sufficient non-lattice oxygen ions in the film play a crucial role in the resistive switching process. The AlOx interface layer is observed to strongly affect the switching mechanism in the memory device by altering the barrier at the Al/ZnO interface. The device shows stable switching behavior for >250 cycles with good retention and stable set/reset voltages.

Forming-free high-endurance Al/ZnO/Al memristor fabricated by dual ion beam sputtering

Single synaptic device with inherent learning and memory functions is demonstrated based on a forming-free amorphous Y2O3 (yttria) memristor fabricated by dual ion beam sputtering system. Synaptic functions such as nonlinear transmission characteristics, long-term plasticity, short-term plasticity and 'learning behavior (LB)' are achieved using a single synaptic device based on cost-effective metal-insulator-semiconductor (MIS) structure. An 'LB' function is demonstrated, for the first time in the literature, for a yttria based memristor, which bears a resemblance to certain memory functions of biological systems. The realization of key synaptic functions in a cost-effective MIS structure would promote much cheaper synapse for artificial neural network.

https://iopscience.iop.org/article/10.1088/1361-6528/aaa0eb/pdf

Realization of synaptic learning and memory functions in Y2O3 based memristive device fabricated by dual ion beam sputtering.

Architecture tailoring of MoO3 nanostructures for superior ethanol sensing performance

The development of a new type of hybrid material comprising naphthalene-based π-conjugated amine (NBA) and zinc oxide (ZnO) nanohybrid, grown in situ on polydimethylsiloxane (PDMS) flexible substra...

π-Conjugated Amine-ZnO Nanohybrids for the Selective Detection of CO2 Gas at Room Temperature

In this report, we study factors that dominate the mode transformation of resistive switching (RS) in yttria based memristive devices. It is found that amorphous yttria films are more suitable for RS whereas highly crystalline films are counterproductive for RS. The transformation from unipolar to bipolar resistive switching mode is demonstrated in our devices via moving from a system of single Schottky barrier diode (SBD) to double SBD. The conduction mechanism behind these transformation mechanisms is found to be predominantly interfacial. We also report a forming-free Al/Y2O3/Al based memristor fabricated by the dual ion beam sputtering without any post-processing steps for the first time. It shows stable switching behavior for >29 000 cycles with good retention (105 s) characteristics.

Mangal Das

Papers

Forming-free high-endurance Al/ZnO/Al memristor fabricated by dual ion beam sputtering

Realization of synaptic learning and memory functions in Y2O3 based memristive device fabricated by dual ion beam sputtering.

Architecture tailoring of MoO3 nanostructures for superior ethanol sensing performance

π-Conjugated Amine-ZnO Nanohybrids for the Selective Detection of CO2 Gas at Room Temperature

Impact of Schottky junctions in the transformation of switching modes in amorphous Y2O3-based memristive system