This paper presents a novel wide tuning range CMOS Voltage Controlled Oscillator (VCO). VCO uses an on-chip variable inductor as an additional variable element to extend the tuning range of VCO. The fabricated variable inductor achieves the variable range of 35%. The VCO was fabricated using 0.35 µm standard CMOS process, and can be tuned continuously from 2.13 GHz to 3.28 GHz (tuning range of 38%) without degradation of phase noise. Wide tunable LC-VCO using a variable inductor is one of the key circuits for reconfigurable RF circuit.

This paper discusses the design and performance of on-chip passive components of transmission lines (TR) and inductors. First, the measurement technique of on chip passives is discussed. A transmission line that can be used for Gbps signal propagation on Si CMOS is examined. As a high density transmission line structure of diagonal-pair differential TR line is described. Also, a circuit and TR line is introduced for above 10 Gbps signal propagation. The on-chip inductor which is a key passive component in RF application of Si CMOS technology is discussed. We examine some on-chip inductors that have been developed in our group: small area inductor, high performance inductor using WL-CSP (Wafer-Level Chip-Size-Packaging) technology. Finally, a wide tuning range LC-VCO using a variable inductor for RF reconfigurable circuit is introduced.

We investigate the possibility of reducing router power consumption through dynamic router performance control. The proposed algorithm employs a typical low pass filter and, therefore, is simple enough to implement in each related element in a router. Numerical experiments using several real Internet traffic data sets show the degree of reduction in power consumption that can be achieved by using the proposed dynamic performance control algorithm. Detailed analysis clarifies the relationships among various parameter values that include packet loss ratios and the degree of power savings. We also propose a simple method based on the leaky bucket model, which can instantaneously estimate the packet loss ratio. It is shown that this simple method yields a good approximation of the results obtained by exact packet-by-packet simulation. The simple method easily enables us to derive appropriate parameter values for the control algorithm for given traffic that may differ in different segments of the Internet.

A method to predict lightning by machine learning analysis of atmospheric electric fields is proposed for the first time. In this study, we calculated an anomaly score with long short-term memory (LSTM), a recurrent neural network analysis method, using electric field data recorded every second on the ground. The threshold value of the anomaly score was defined, and a lightning alarm at the observation point was issued or canceled. Using this method, it was confirmed that 88.9% of lightning occurred while alarming. These results suggest that a lightning prediction system with an electric field sensor and machine learning can be developed in the future.

This paper proposes high-Q distributed constant passive devices using wafer-level chip scale package (WL-CSP) technology, which can be realized on a Si CMOS chip. A 90directional coupler using the WL-CSP technology has center frequency of 25.6 GHz, insertion loss of -0.5 dB and isolation of -29.8 dB in the measurement result. The WL-CSP technology contributes to realize low-loss RF passive devices on Si CMOS chip, which is indispensable to achieve small-size, cost-effective and low-power monolithic wireless communication circuits (MWCCs).

This paper proposes a low-phase-noise ring-VCO-based frequency multiplier with a new subharmonic direct injection locking technique that only uses a time-delay cell and four MOS transistors. Since the proposed technique behaves as an exclusive OR and can double the reference signal frequency, it increases phase correction points and achieves low phase noise characteristic across the wide output frequency range. The frequency multiplier was fabricated by using 65 nm Si CMOS process. Measured 1-MHz-offset phase noise at 6.34 GHz with reference signals of 528 MHz was -119 dBc/Hz.

This paper proposes an ultra-low-power 5.5-GHz PLL which employs the new divide-by-4 injection-locked frequency divider (ILFD) and a class-C VCO with linearity-compensated varactor for low supply voltage operation. A forward-body-biasing (FBB) technique can decrease threshold voltage of MOS transistors, which can improve operation frequency and can widen the lock range of the ILFD. The FBB is also employed for linear-frequency-tuning of VCO under low supply voltage of 0.5V. The double-switch injection technique is also proposed to widen the lock range of the ILFD. The digital calibration circuit is introduced to control the lock-range of ILFD automatically. The proposed PLL was fabricated in a 65nm CMOS process. With a 34.3-MHz reference, it shows a 1-MHz-offset phase noise of -106dBc/Hz at 5.5GHz output. The supply voltage is 0.54V for divider and 0.5V for other components. Total power consumption is 0.95mW.

The present paper proposes differential transmission line structures on Si ULSI. Interconnect structures are examined using numerical results from a two-dimensional electromagnetic simulation (Ansoft, 2D Extractor). The co-planar and diagonal-pair lines are found to have superior characteristics for gigahertz signal propagation through long interconnects. The proposed diagonal-pair line can reduce the crosstalk noise and interconnect resource concurrently.

Cow behavior monitoring is critical for understanding the current state of cow welfare and developing an effective planning strategy for pasture management, such as early detection of disease and estrus. One of the most powerful and cost-effective methods is a neural-network-based monitoring system that analyzes time series data from inertial sensors attached to cows. For this method, a significant challenge is to improve the quality and quantity of teaching data in the development of neural network models, which requires us to collect data that can cover various realistic conditions and assign labels to them. As a result, the cost of data collection is significantly high. This work proposes a data augmentation method to solve two major quality problems in the collection process of teaching data. One is the difficulty and randomicity of teaching data acquisition and the other is the sensor position changes during actual operation. The proposed method can computationally emulate different rotating states of the collar-type sensor device from the measured acceleration data. Furthermore, it generates data for actions that occur less frequently. The verification results showed significantly higher estimation performance with an average accuracy of over 98% for five main behaviors (feeding, walking, drinking, rumination, and resting) based on learning with long short-term memory (LSTM) network. Compared with the estimation performance without data augmentation, which was insufficient with a minimum of 60.48%, the recognition rate was improved by 2.52-37.05pt for various behaviors. In addition, comparison of different rotation intervals was investigated and a 30-degree increment was selected based on the accuracy performances analysis. In conclusion, the proposed data expansion method can improve the accuracy in cow behavior estimation by a neural network model. Moreover, it contributes to a significant reduction of the teaching data collection cost for machine learning and opens many opportunities for new research.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

Ocean surface current radar is a Doppler radar to observe oceanographic information using the Bragg scattering resonance mechanism. In this paper, we consider angular resolution improvement of the radar. The radar employs an antenna array with FMICW operation, then it can resolve angular distribution by Digital Beam Forming (DBF) and distance by Fourier transform of the beat signal obtained by the FMICW radar. In order to obtain sufficient angular resolution, large array length or aperture with increasing the number of elements is needed, that is often difficult to realize in the HF/VHF ocean surface current radar. In this paper we propose to apply the Khatri-Rao (KR) product array processing to the radar. To verify effectiveness of the KR product array processing in angular resolution enhancement for the ocean surface current radar, we apply the KR product array to actual experimental data set of the radar, and show that the method is available to angular resolution enhancement and Doppler spectrum improvement.