The characteristics of HfO 2 films grown on Si substrates using a tetrakis-diethyl-amino-hafnium precursor by the remote plasma atomic layer deposition ͑RPALD͒ and direct plasma ALD ͑DPALD͒ methods were investigated by physical and electrical measurement techniques. The as-deposited HfO 2 layer from RPALD exhibits an amorphous structure, while the HfO 2 layer from DPALD exhibits a clearly visible polycrystalline structure. Medium energy ion scattering measurement results indicate that the interfacial layer consists of the interfacial SiO 2−x and silicate layers. These results suggested that the stoichiometric change in the depth direction could be related to the energetic reactant in a state of plasma used in the plasma ALD process, resulting in damage to the Si surface and interactions between Hf and SiO 2−x . The as-deposited HfO 2 films using RPALD have the better interfacial layer characteristics than those using DPALD. A metal-oxide-semiconductor capacitor fabricated using the RPALD method exhibits electrical characteristics such as equivalent oxide thickness ͑EOT͒ of 1.8 nm with an effective fixed oxide charge density ͑Q f,eff ͒ of ϳ4.2 ϫ 10 11 q/cm 2 and that for DPALD has a EOT ͑2.0 nm͒, and Q f,eff ͑ϳ−1.2ϫ 10 13 q/cm 2 ͒.

† These authors contributed equally to this work.HighlightsIn this paper, the necessity of congestion estimation in long range IoT applications is described. Congestion Classifier using Logistic Regression and the modified adaptive data rate control scheme is designed.-For the validation of the efficiency of the data transmission, analysis on the transmission delay is carried out. Results show that the proposed method outperforms state of the art methods.-In this way, the proposal improves transmission efficiency in aspect of the transmission delay in wireless environment where congestion occurs.Our proposed method predicts congestion status by learning and determines whether a node drops data rate or not. Thus, it leads to avoiding unnecessary change of data rate.Through analysis on transmission delay, the proposed scheme has shown that it is the proper data rate control method for IoT networking in congestion environment. AbstractInternet of Things (IoT) technologies can provide various intelligent services by collecting information from objects. To collect information, Wireless Sensor Networks (WSNs) are exploited. The Low Power Wide Area Network (LPWAN), one type of WSN, has been designed for long-range IoT services. It consumes low power and uses a low data rate for data transmission. The LPWAN includes several communication standards, and Long Range Wide Area Network (LoRaWAN) is the representative standard of the LPWAN. LoRaWAN provides several data rates for transmission and enables adaptive data rate control in order to maintain network connectivity. In the LoRaWAN, the wireless condition is considered by the reception status of the acknowledgement (ACK) message, and adaptive data rate control is performed according to the wireless condition. Because the judgment of the wireless condition by the reception status of ACK messages does not reflect congestion, adaptive data rate control can lead to inefficiency in data transmission. For efficient data transmission in long-range IoT services, this paper proposes a congestion classifier using logistic regression and modified adaptive data rate control. The proposed scheme controls the data rate according to the congestion estimation. Through extensive analysis, we show the proposed scheme's efficiency in data transmission.

We deposited HfO 2 , ZrO 2 , and Zr x Hf 1−x O 2 films having different ZrO 2 contents on Si substrates by atomic layer deposition at 300°C and investigated their physical and electrical characteristics. The HfO 2 and ZrO 2 films with thicknesses of about 20 nm exhibited crystalline structures composed of monoclinic and tetragonal phases, respectively. As the ZrO 2 content in the hafniumzirconium-oxide was increased, the ratio of the tetragonal phase seen in the crystal increased. These changes in crystal phase led to changes in electrical properties. The crystalline phases and electrical properties of the hafnium-zirconium-oxide films exhibited a strong dependence on their Hf/Zr composition ratio.

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Hafnium oxide thin films were deposited using both the remote-plasma atomic layer deposition (RPALD) and direct-plasma atomic layer deposition (DPALD) methods. Metal-oxide semiconductor (MOS) capacitors and transistors were fabricated with HfO2 gate dielectric to examine their electrical characteristics. The as-deposited RPALD HfO2 layer exhibited an amorphous structure, while the DPALD HfO2 layer exhibited a polycrystalline structure. Medium-energy ion scattering measurement data indicate that the interfacial layer consisted of interfacial SiO2−x and silicate layers. This suggests that the change in stoichiometry with depth could be related to the energetic plasma beam used in the plasma ALD process, resulting in damage to the Si surface and an interaction between Hf and SiO2−x. The as-deposited RPALD HfO2 films had better interfacial layer characteristics, such as an effective fixed oxide charge density (Qf,eff) and interfacial roughness than the DPALD HfO2 films did. A MOS capacitor fabricated using the RPALD method exhibited an equivalent oxide thickness (EOT) of 1.8nm with a Qf,eff=−4.2×1011q∕cm2 (where q is the elementary charge, 1.6022×10−19C), whereas a MOS capacitor fabricated using the DPALD method had an EOT=2.0nm and a Qf,eff=−1.2×1013q∕cm2. At a power=0.6MV∕cm, the RPALD n-type metal-oxide semiconductor field-effect transistor (nMOSFET) showed μeff=168cm2∕Vs, which was 50% greater than the value of the DPALD nMOSFET (μeff=111cm2∕Vs). In the region where Vg-Vt=2.0V, the RPALD MOSFET drain current was about 30% higher than the DPALD MOSFET drain current. These improvements are believed to be due to the lower effective fixed charge density, and they minimize problems arising from plasma charging damage.

A remote plasma atomic layer deposition (RPALD) method has been applied to grow a hafnium oxide thin film on the Si substrate. The deposition process was monitored by in situ XPS and the as-deposited structure and chemical bonding were examined by TEM and XPS. The in situ XPS measurement showed the presence of a hafnium silicate phase at the initial stage of the RPALD process up to the 20th cycle and indicated that no hafnium silicide was formed. The initial hafnium silicate was amorphous and grew to a thickness of approximately 2nm. Based on these results and model reactions for silicate formation, we proposed an initial growth mechanism that includes adatom migration at nascent step edges. Density functional theory calculations on model compounds indicate that the hafnium silicate is thermodynamically favored over the hafnium silicide by as much as 250kJ∕mol.

The physical and electrical characteristics of HfO 2 dielectrics deposited by the plasma-enhanced atomic layer deposition ͑PEALD͒ method were investigated. The HfO 2 films that were deposited with N 2 O plasma showed a wider atomic layer deposition process window and a higher growth rate than those deposited with N 2 O gas. Nitrogen atoms were successfully incorporated into the interface between the HfO 2 films and Si substrate during the PEALD process without requiring an additional nitridation process prior to the HfO 2 deposition. The nitrogen atoms in the interfacial region of the HfO 2 effectively blocked oxygen diffusion during subsequent annealing in a N 2 atmosphere. As-deposited HfO 2 films with N 2 O gas had an amorphous structure while those with N 2 O plasma contained a randomly oriented polycrystalline phase of HfO 2 . The oxide-trapped charge densities for the as-deposited HfO 2 films with N 2 O gas andN 2 O plasma were 1.3 ϫ 10 13 and 9.8 ϫ 10 11 cm −2 , respectively. The equivalent oxide thickness of the as-deposited HfO 2 films with N 2 O gas and N 2 O plasma were approximately 1.55 and 1.43 nm, respectively. The leakage current densities of the films was 1.2 ϫ 10 −6 and 1.2 ϫ 10 −7 A/cm 2 for N 2 O gas and N 2 O plasma, respectively.As advanced semiconductor devices are scaled to dimensions below 0.1 m, current gate dielectrics, such as SiO 2 must be scaled down below 20 Å. 1,2 Unfortunately, this projected scaling of SiO 2 generates leakage-induced problems. 3 Thus, materials with high dielectric constants have recently been extensively studied as possible alternatives to SiO 2 . The main advantages of high-dielectric constant materials are an increase in physical thickness and a reduction of leakage currents without a significant increase in the equivalent oxide thickness ͑EOT͒ of gate dielectrics. 4 Among the many materials with a high dielectric constant, HfO 2 , ZrO 2 , Al 2 O 3 , and their silicates have been extensively studied for incorporation into a future generation of semiconductor devices with the aim of eliminating excessively high leakage current. Of these materials, HfO 2 is considered to be one of the attractive candidates. HfO 2 has several desirable properties, including a high dielectric constant ͑ = 25-30͒, high density ͑ϳ9.65 g/cm 3 ͒, a large bandgap ͑5.68 eV͒, and thermal stability when in contact with silicon. 5-8 However, the growth of an interfacial layer in the HfO 2 films is still likely to occur due to interfacial diffusion and reactions during deposition or postdeposition annealing. 9,10 This interfacial layer generally has a dielectric constant lower than HfO 2 alone; therefore, it is necessary to reduce the growth of the interfacial layer during deposition and postannealing. With this goal in mind, we utilized a nitrided Si surface because the nitrided layer exhibited a high dielectric constant while suppressing the growth of an interfacial layer by retarding oxygen diffusion. However, thermal nitridation in N 2 O, NO, and NH 3 gas atmospheres requires annealing ...

Abstract:In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naïve Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown.