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A PRAM write driver with an auto-scaling overdrive method is presented. The proposed overdrive method significantly reduces the rise time of the cell-current pulse for bit-line parasitic components of 3 pF and 6 k $\Omega$ , and it lowers the complexity of the overdrive control using an adaptive charge amplification technique. A rise time of less than 15 ns is achieved and shortened up to 4.7 times, and the total write-throughput is increased. The rise time is reduced consistently for all levels of the target-current by the auto-scaling effect. Therefore, cell heating control becomes more linear in program-and-verify (PNV) operation. Due to its simple adding-on structure, it is easily compatible with a conventional write driver. A prototype chip was implemented using a 0.18- $\mu$ m CMOS process. It is also applicable to smaller-scale technology.

This paper presents a 65-nm CMOS low-dropout (LDO) regulator employing a super gain amplifier (SGA) and differential feed-forward noise cancellation to maximize the power supply rejection (PSR). The SGA in the error amplifier is augmented by a positive feedback current mirror, and this SGA boosts the loop gain through local negative feedback. With 1.2 V supply voltage, the LDO regulator has a 200 mV drop-out voltage and the ability to handle a maximum 25 mA load current. The measurement results show a -47 dB PSR ratio of up to 10 MHz and dc load regulation under 1 mV for full load current change.

This paper reports a label-free biosensor for the detection of DNA hybridization. The proposed biosensor measures the surface potential on oligonucleotide modified electrodes using a direct charge accumulation method. The sensor directly and repeatedly measures the charges induced in the working electrode, which correspond to intrinsic negative charges in immobilized molecules. The sensor achieves an improved signal-to-noise ratio (SNR), through the oversampling effect of accumulation for charges and the differential architecture. The sensor also shows stable, robust, and reproducible measurement independent of slight changes in the reference voltage, unlike previous ion-sensitive field effect transistors (ISFETs), providing the benefits of choosing a wide variety of reference electrode materials. The proposed device is integrated with working electrodes, a reference electrode and readout circuits into one package via a 0.35 μm complementary metal-oxide-semiconductor (CMOS) process. The sensor achieves a detectable range of 88.3 dB and a detection limit of 36 μV for surface potential. It is demonstrated that the sensor successfully achieves specific detection of oligonucleotide sequences derived from the H5N1 avian influenza virus. The experiments show a limit of detection of 100 pM and include a single-base mismatch test in 18-mer oligonucleotides.

This paper describes a sensor for label-free, fully electrical detection of DNA hybridization based on capacitive changes in the electrode-electrolyte interface. The sensor measures capacitive changes in real time according to a charging-discharging principle that is limited by the hysteresis window. In addition, a novel autonomous searching technique, which exclusively monitors desorption-free hybridized electrodes among electrode arrays, enhances the performance of the sensor compared with conventional capacitive measurement. The sensor system achieves a detection range of 80 dB. The integrated circuit sensor is fabricated with a 0.35 μm CMOS process. The proposed sensor offers rapid, robust and inexpensive measurement of capacitance with highly integrated detection circuitry. It also facilitates quantitative evaluations of molecular densities on a chip with distinctive impedance variations by monitoring desorption-free hybridized electrodes. Our electrical biosensor has great potential for use with bio analytical tools and point-of-care diagnosis.

Gears are common mechanical components used in power transmissions and frequently responsible for transmission failures. The aim of this study is to establish a gear crack prognostic methodology to predict the residual life of a cracked spur gear by integrating a fracture mechanics-based failure model, a gear dynamic simulator, and an existing gear crack diagnostic algorithm that employs an artificial neural network to estimate crack size from measured gear vibration by fusing a number of selected gear condition indices. The estimated crack size and predicted gear residual life were validated with experimental data. Experimental results showed that the method had an averaged error of 12.94 per cent in its prediction of residual life.

Gears are common power transmission elements and are frequently responsible for transmission failures. Since a tooth crack is not directly measurable while a gear is in operation, one has to develop an indirect method to estimate its size from some measurables. This study developed such a method to estimate the size of a tooth transverse crack for a spur gear in operation. Using gear vibrations measured from an actual gear accelerated test, this study examined existing gear condition indices to identify those correlated well to crack size and established their utility for crack size estimation through index fusion using a neural network. When tested with vibrations measured from another accelerated test, the method had an averaged estimation error of about 5%.

This paper presents a new control scheme dubbed Bang-Bang Zeroth-Order Control (BBZOC) for Single Inductor Multiple Output (SIMO) buck converter. The main loop control utilizes a phase detector, charge pump, filter, and comparator. The SIMO buck converter with BBZOC simplifies the compensation design compared to conventional voltage mode control. This work is fabricated in 1P4M 0.35um BCD process and achieves 83% maximum efficiency with the rated output power of 1.04W. The maximum output power is 2.7W and the maximum power density is 1.07 W/mm2. Considering the difference in the process, this work represents the state of the art in the power density.

Energy harvesting is one of the key technologies used to realize self-sustaining systems such as wireless sensor networks and health-care devices. Much research on circuit design has been conducted to extract as much energy as possible from transducers, such as the thermoelectric generator (TEG) and the piezoelectric transducer (PZT). Specifically, the energy in a PZT could be extracted more efficiently by utilizing resonance as [1] and [2] demonstrated. However, the maximum output voltage swing in those techniques are limited to twice of the original swing of the PZT, and thus, had a limited energy extraction capability in spite of more energy being available from the PZT. In [3], on the other hand, the large energy is obtained with higher voltage swing, but is limited up to 247% because the load energy is used to increase the output voltage swing of PZT. To obtain far more power from PZT, we propose an alternative resonance technique through which the PZT output swing can be boosted as high as CMOS devices can sustain. This technique is applied to a dual-energy-sourced (PZT and TEG) energy-harvesting interface (EHI) as a battery charger.

Silicon devices based on impedance measurements offer label-free and direct electrical detection when used to quantify the hybridization of DNA molecules. They show rapid, robust, and inexpensive measurement and compatibility with commercial microfabrication technology. The real-time measurement of the impedance does not require the use of labeling molecules attached to the target DNA in optical and magnetic technology [1,2]. It also has the advantage of miniaturization for point-of-care (PoC) or on-site sensing applications, unlike the 3-electrode topology in electrochemical sensors [3]. Several studies have proposed capacitive biosensors that utilize a nonfaradaic process, which refers to transient currents charging a geometrical capacitor in an electrolyte-electrode interface [4]. Conventional capacitive biosensors using the excitation of the bidirectional current [5,6] can be implemented with a compact design, but they have several issues that degrade the sensitivity of the sensor, such as DC drift in the electrode caused by a charge imbalance, the electrolysis generated by DC voltage across the electrodes, the offset generated by pre-charged initial values, and weakness against common-mode noise. As a solution, we report a fully integrated capacitance-based biosensor that locates two electrodes differentially in a single current source.

This paper describes a label-free and fully electronic detection method of DNA hybridization, which is achieved through the use of a 16 × 8 microarray sensor in conjunction with a new type of impedance spectroscopy constructed with standard complementary metal-oxide-semiconductor (CMOS) technology. The impedance-based method is based on changes in the reactive capacitance and the charge-transfer resistance after hybridization with complementary DNA targets. In previously published label-free techniques, the measured capacitance presented unstable capacitive properties due to the parallel resistance that is not infinite and can cause a leakage by discharging the charge on the capacitor. This paper presents an impedance extraction method that uses excitation by triangular wave voltage, which enables a reliable measurement of both C and R producing a highly sensitive sensor with a stable operation independent of external variables. The system was fabricated in an industrial 0.35-μm 4-metal 2-poly CMOS process, integrating working electrodes and readout electronics into one chip. The integrated readout, which uses a parasitic insensitive integrator, achieves an enlarged detection range and improved noise performance. The maximum average relative variations of C and R are 31.5% and 68.6%, respectively, after hybridization with a 1 μM target DNA. The proposed sensor allows quantitative evaluation of the molecule densities on the chip with distinguishable variation in the impedance. This fully electronic microsystem has great potential for use with bioanalytical tools and point-of-care diagnosis.

Gears are common mechanical components used in power transmissions and frequently responsible for transmission failures. Since the crack size is not directly measurable while gear is in service, there is difficulty in predicting residual life of gears in service. The aim of this study is to present a prognostic methodology to predict the residual life of a cracked spur gear that is in service utilizing 2D FEM/fracture mechanics based failure model. The previously developed gear diagnostic algorithms and artificial neural network were employed to estimate crack size from measured gear vibration. The predicted results were validated with experimental data. Experimental results showed that the method had an averaged error of 12.94% in its estimate of remaining life.Copyright © 2005 by ASME