Space-Compliant Design of a Millimeter-Wave GaN-on-Si Stacked Power Amplifier Cell through Electro-Magnetic and Thermal Simulations

The stacked power amplifier is a widely adopted solution in CMOS technology to overcome breakdown limits. Its application to compound semiconductor technology is instead rather limited especially at very high frequency, where device parasitic reactances make the design extremely challenging, and in gallium nitride technology, which already offers high breakdown voltages. Indeed, the stacked topology can also be advantageous in such scenarios as it can enhance gain and chip compactness. Moreover, the higher supply voltages and lower supply currents beneficially impact on reliability, thus making the stacked configuration an attractive solution for space applications. This paper details the design of two stacked cells, differing in their inter-stage matching strategy, conceived for space applications at Ka-band in 100 nm GaN-on-Si technology. In particular, the design challenges related to the thermal constraints posed by space reliability and to the electro-magnetic cross-talk issues that may arise at millimeter-wave frequencies are discussed. The best cell achieves at saturation, in simulation, 3 W of output power at 36 GHz with associated gain and efficiency in excess of 7 dB and 35%, respectively

Characterization of the Dynamic RON of 600 V GaN Switches under Operating Conditions

High-voltage GaN switches can offer tremendous advantages over silicon counterparts for the development of high-efficiency switching-mode power converters at high commutation frequency. Nonetheless, GaN devices are prone to charge-trapping effects that can be particularly relevant in the early-stage development of new technologies. Charge-trapping mechanisms are responsible for the degradation of the dynamic ON-resistance (RON) with respect to its static value: this degradation is typically dependent on the blocking voltage, the commutation frequency and temperature, and is responsible for the reduction of power converter efficiency. The characterization of this phenomenon is very valuable for the development of a new process to compare different technological solutions or for the final assessment of performance. This characterization cannot be made with traditional static or small signal measurements since RON degradation is triggered by application-like dynamic device excitations. In this paper, we propose a technique for the characterization of the dynamic RON of high-voltage GaN switches under real operating conditions: this technique is based on the design of a half bridge switching leg in which the DUT is operated under conditions that resemble its operation in a power converter. With this setup, the characterization of a 600 V GaN switch dynamic RON is performed as a function of variable blocking voltages and commutation frequency. Additionally, this technique allows the separation of thermal and trapping effects, enabling the characterization of the dynamic RON at different temperature

Design of a 350 kW DC/DC Converter in 1200-V SiC Module Technology for Automotive Component Testing

In this paper, the design and implementation of a DC/DC converter for automotive component testing with state-of-the art performance is described. The converter is the core of a battery emulator for the characterization and development of automotive batteries, electronic chargers, traction inverters, DC-DC converters, E-motors and E-axles. Cutting edge performance, flexibility and compactness are obtained by exploiting 1200-V SiC modules, high switching frequency, planar transformer technology, suitable topology solutions and fast digital control strategies. The implemented system is a liquid-cooled, bidirectional converter with galvanic isolation capable of 350 kW continuous output power, output voltage range 48-1000 V, continuous output current up to 800 A (1600 A peak), voltage/current ramp-up time below 10/2 ms and 0.1% current/voltage accuracy. The entire instrument is implemented in a standard full-height 19-inch rack cabinet

Beam-Dependent Active Array Linearization by Global Feature-Based Machine Learning

An approach based on machine learning is proposed for the global linearization of microwave active beamforming arrays. The method allows for the low-complexity real-time update of the digital predistortion (DPD) coefficients by exploiting order-reduced model features, hence avoiding the need for repeated local DPD identification steps across the various operating conditions of the beamformer (e.g., different beam angles or RF power levels). The validation is performed by over-the-air (OTA) measurements of a 1×4 array operating at 28 GHz across 100-MHz modulation bandwidth (BW)

Preliminary validity of the Draw a Shape Test for upper extremity assessment in multiple sclerosis

Multiple sclerosisEsclerosi múltipleEsclerosis múltipleObjective To validate the smartphone sensor-based Draw a Shape Test – a part of the Floodlight Proof-of-Concept app for remotely assessing multiple sclerosis-related upper extremity impairment by tracing six different shapes. Methods People with multiple sclerosis, classified functionally normal/abnormal via their Nine-Hole Peg Test time, and healthy controls participated in a 24-week, nonrandomized study. Spatial (trace accuracy), temporal (mean and variability in linear, angular, and radial drawing velocities, and dwell time ratio), and spatiotemporal features (trace celerity) were cross-sectionally analyzed for correlation with standard clinical and brain magnetic resonance imaging (normalized brain volume and total lesion volume) disease burden measures, and for capacity to differentiate people with multiple sclerosis from healthy controls. Results Data from 69 people with multiple sclerosis and 18 healthy controls were analyzed. Trace accuracy (all shapes), linear velocity variability (circle, figure-of-8, spiral shapes), and radial velocity variability (spiral shape) had a mostly fair/moderate-to-good correlation (|r| = 0.14–0.66) with all disease burden measures. Trace celerity also had mostly fair/moderate-to-good correlation (|r| = 0.18–0.41) with Nine-Hole Peg Test performance, cerebellar functional system score, and brain magnetic resonance imaging. Furthermore, partial correlation analysis related these results to motor impairment. People with multiple sclerosis showed greater drawing velocity variability, though slower mean velocity, than healthy controls. Linear velocity (spiral shape) and angular velocity (circle shape) potentially differentiate functionally normal people with multiple sclerosis from healthy controls. Interpretation The Draw a Shape Test objectively assesses upper extremity impairment and correlates with all disease burden measures, thus aiding multiple sclerosis-related upper extremity impairment characterization.This research was funded by F. Hoffmann-La Roche Ltd, Basel, Switzerland

U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment

Background: People living with multiple sclerosis (MS) experience impairments in gait and mobility, that are not fully captured with manually timed walking tests or rating scales administered during periodic clinical visits. We have developed a smartphone-based assessment of ambulation performance, the 5 U-Turn Test (5UTT), a quantitative self-administered test of U-turn ability while walking, for people with MS (PwMS). Research question: What is the test-retest reliability and concurrent validity of U-turn speed, an unsupervised self-assessment of gait and balance impairment, measured using a body-worn smartphone during the 5UTT? Methods: 76 PwMS and 25 healthy controls (HCs) participated in a cross-sectional non-randomised interventional feasibility study. The 5UTT was self-administered daily and the median U-turn speed, measured during a 14-day session, was compared against existing validated in-clinic measures of MS-related disability. Results: U-turn speed, measured during a 14-day session from the 5UTT, demonstrated good-to-excellent test-retest reliability in PwMS alone and combined with HCs (intraclass correlation coefficient [ICC] = 0.87 [95 % CI: 0.80-0.92]) and moderate-to-excellent reliability in HCs alone (ICC = 0.88 [95 % CI: 0.69-0.96]). U-turn speed was significantly correlated with in-clinic measures of walking speed, physical fatigue, ambulation impairment, overall MS-related disability and patients' self-perception of quality of life, at baseline, Week 12 and Week 24. The minimal detectable change of the U-turn speed from the 5UTT was low (19.42 %) in PwMS and indicates a good precision of this measurement tool when compared with conventional in-clinic measures of walking performance. Significance: The frequent self-assessment of turn speed, as an outcome measure from a smartphone-based U-turn test, may represent an ecologically valid digital solution to remotely and reliably monitor gait and balance impairment in a home environment during MS clinical trials and practice

Preliminary validity of the Draw a Shape Test for upper extremity assessment in multiple sclerosis

Objective To validate the smartphone sensor-based Draw a Shape Test - a part of the Floodlight Proof-of-Concept app for remotely assessing multiple sclerosis-related upper extremity impairment by tracing six different shapes. Methods People with multiple sclerosis, classified functionally normal/abnormal via their Nine-Hole Peg Test time, and healthy controls participated in a 24-week, nonrandomized study. Spatial (trace accuracy), temporal (mean and variability in linear, angular, and radial drawing velocities, and dwell time ratio), and spatiotemporal features (trace celerity) were cross-sectionally analyzed for correlation with standard clinical and brain magnetic resonance imaging (normalized brain volume and total lesion volume) disease burden measures, and for capacity to differentiate people with multiple sclerosis from healthy controls. Results Data from 69 people with multiple sclerosis and 18 healthy controls were analyzed. Trace accuracy (all shapes), linear velocity variability (circle, figure-of-8, spiral shapes), and radial velocity variability (spiral shape) had a mostly fair/moderate-to-good correlation (|r| = 0.14-0.66) with all disease burden measures. Trace celerity also had mostly fair/moderate-to-good correlation (|r| = 0.18-0.41) with Nine-Hole Peg Test performance, cerebellar functional system score, and brain magnetic resonance imaging. Furthermore, partial correlation analysis related these results to motor impairment. People with multiple sclerosis showed greater drawing velocity variability, though slower mean velocity, than healthy controls. Linear velocity (spiral shape) and angular velocity (circle shape) potentially differentiate functionally normal people with multiple sclerosis from healthy controls. Interpretation The Draw a Shape Test objectively assesses upper extremity impairment and correlates with all disease burden measures, thus aiding multiple sclerosis-related upper extremity impairment characterization

Adherence and satisfaction of smartphone- And smartwatch-based remote active testing and passive monitoring in people with multiple sclerosis : Nonrandomized interventional feasibility study

Background: Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. Objective: The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. Methods: People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. Results: People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. Conclusions: People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings