A power amplifier (PA) using the distributed amplifier technique for the ultra wideband (UWB) standard is presented. The amplifier is fabricated in a standard 0.13 μm CMOS technology and comes with on-chip biasing circuitry and a non-distributed input stage. Measurement results are given for a chip-on-board module to take any influence of product assembly into account. It achieves a transmission coefficient S21 = 17 dB, a corner frequency of fc = 8 GHz and a 1 dB compression point of A1dB = 3.5 dBm. The output impedance is matched to 50 Ω so that external matching circuitry can be omitted. With these features, it is customized to be integrated with other building blocks to a fully integrated CMOS UWB transmitter product

A new low-cost concept for a system-on-chip Bluetooth solution is proposed in this paper. The single chip includes all necessary baseband and RF parts to achieve full Bluetooth functionality and is implemented in a standard 0.25-/spl mu/m CMOS technology. The two-point modulation /spl Sigma//spl Delta/ fractional N phase-locked loop achieves a phase noise of -124 dBc/Hz at 3-MHz offset. The sensitivity of the embedded low-IF receiver is measured to be -82 dBm at a bit error rate of 0.1%. The power supply voltages for the digital and analog parts are internally regulated to 2.65 V. The maximum current consumption of the analog part is 60 mA.

We characterized the thermal behavior of a 65 nm bulk CMOS transistor, by measuring the self-heating effect (SHE) as a function of bias condition. We demonstrated that at a base temperature of 6.5 K the channel temperature of the transistor can increase up to several tens of kelvins due to power dissipation. The thermal behavior of the transistor is determined not only by the thermal response of the transistor itself but also by the thermal properties of the surroundings, i.e., source, drain, bulk, and gate interfaces, metal contacts, and vias. On top of it, the thermal response is bias-dependent through bias dependence of power and self-heating. This information becomes relevant for proper design of integrated circuits for quantum computing or other cryogenic applications, where the circuitry requires to be operated at a stable cryogenic temperature.

A scalable eight channel DAC designed in a TSMC 65nm CMOS technology for generation of solid-state quantum bit (qubit) bias voltages is presented. Measurement results of the DAC and some additional auxiliary components like an on-chip amplifier and Sigma-Delta modulator at 6 K are discussed. With a low power consumption of 2.7 μW per channel, the DAC fulfills the requirements to be placed next to qubits inside a mixing chamber of a dilution refrigerator, showing a promising way for scaling qubit numbers towards a quantum computer.

Goal: Thermography-based infection screening at international airports plays an important role in the prevention of pandemics. However, studies show that thermography suffers from low sensitivity and specificity. To achieve higher screening accuracy, we developed a screening system based on the acquisition of multiple vital-signs. This multimodal approach increases accuracy, but introduces the need for sophisticated classification methods. This paper presents a comprehensive analysis of the multimodal approach to infection screening from a machine learning perspective. Methods: We conduct an empirical study applying six classification algorithms to measurements from the multimodal screening system and comparing their performance among each other, as well as to the performance of thermography. In addition, we provide an information theoretic view on the use of multiple vital-signs for infection screening. The classification methods are tested using the same clinical data, which has been analyzed in our previous study using linear discriminant analysis. A total of 92 subjects were recruited for influenza screening using the system, consisting of 57 inpatients diagnosed to have seasonal influenza and 35 healthy controls. Results: Our study revealed that the multimodal screening system reduces the misclassification rate by more than 50% compared to thermography. At the same time, none of the multimodal classifiers needed more than 6 ms for classification, which is negligible for practical purposes. Conclusion: Among the tested classifiers k-nearest neighbors, support vector machine and quadratic discriminant analysis achieved the highest cross-validated sensitivity score of 93%. Significance: Multimodal infection screening might be able to address the shortcomings of thermography.