Recently, many applications have been developed using mm-Wave devices with different specifications and requirements. The semiconductor manufacturing technology has improved the bandwidth and power delivery of integrated circuits. For mm-wave devices, on-board or on-chip antennas are often the performance limiting factors. With wide-ranging specifications, automation of antenna designs has become necessary to find the optimal sizing and materials. Typically, design optimization relies on being able to conduct many simulations to evaluate the performance of design instances. However, electromagnetic (EM) simulations are very time-consuming. Surrogate model-based techniques have been developed to minimize the number of simulations. For large sets of design parameters and large input space, relying on a single model may not provide the best solution. In this paper, we propose an evolutionary search-based algorithm for the multi-objective antenna design optimization problem using multiple surrogate models to determine candidates for EM simulations. The proposed method models the reflection coefficient as a function of frequency and antenna gain as a function of angle and tries to concurrently maximize bandwidth and field of view. Two architectures have been designed for evaluation with 5 and 14 design parameters. The proposed technique can provide a large bandwidth and field of view with only several hundred samples.

RF systems, including RF MIMO RADARs, are increasingly integrated with digital systems in fine-geometry processes. Due to the prevalent use of RF MIMO RADARs in automotive and other safety-critical applications, in-field testing and tuning of these systems are needed to meet performance and safety targets. The fundamental performance targets of an RF MIMO system include the signal-to-noise ratio at the end of the receiver chain, matching characteristics between different signal paths, gain, noise figure, and linearity of the RF front end. In a RADAR device, matching between signal paths affects the angular resolution of the system. The gain and noise figure of the receiver control the maximum distance and the smallest object that the system can detect. In this work, we present a global tuning algorithm for RF MIMO RADARs to meet critical system performance targets while minimizing power consumption. The efficacy of the method is demonstrated with extensive simulations and hardware experiments.

Hardware imperfections and environmental factors create mismatches between transmit and receive paths. In MIMO mm-Wave radars, determining and eliminating gain and phase mismatches are required to increase the overall accuracy of range and angle of arrival (AoA) estimation. Measurement of mismatches, particularly phase mismatch, requires complex test setups and external equipment, such as a network analyzer. This paper proposes an on-chip (or on-board) measurement method for mm-Wave radars to determine the mismatches using RF power detectors. The proposed method relies on mutual coupling between transmitter and receiver antennas. A detailed mathematical analysis of the proposed method along with boundary conditions is presented. Simulations and hardware measurements using a cascaded mm-Wave radar device shows that the proposed phase mismatch extraction technique provides very accurate results within defined boundary conditions.

Millimeter-wave radars are used to estimate the position of an object relative to the radar position in terms of the range, R, azimuth angle, θ, and elevation angle, ϕ. Typical radar operation includes transmitting a chirp signal, receiving the signal reflected by the objects in the environment, and mixing these signals at the receiver chain. For multiple antenna systems, the range can be calculated for each transmit and receive antenna, yielding multiple measurements. To increase the accuracy, these results are averaged. Angle estimation makes use of the phase differences between different antenna paths. Since there is only one calculation across all antenna elements, this calculation does not benefit from averaging. In addition to these random errors, systemic errors occur in the process of angle calculation. In this paper, we analyze the root cause of the systemic error and propose solutions to correct for this error to increase angle estimation accuracy.

Due to increasing performance demands, RF circuits are increasingly integrated into fine-geometry processes which necessitates their post-production and in-field calibration. One of the most important parameters for RF performance is matching between various nodes in the circuit. Process, voltage, and temperature (PVT) variations and other environmental conditions such as objects in the near-field of an antenna can cause shifts in the RF matching between sources and the loads. To compensate for such shifts, generally a tunable element or a network is incorporated between the source and the load, which will enable post-production calibration. In this paper, we present a tuning algorithm that adjusts the tuning structures until a match is detected via built-in measurements. The proposed technique can be used both for post-production and for in-field calibration. It only requires three strategically placed power detectors along the transmission line connecting the source to the load and simple computations. Experimental results based on simulations and hardware measurements show that the technique can provide highly accurate matching between a source and a load component.

This paper discusses how adaptive test techniques can be applied to multi-core RF SoCs, together with design implementation and test challenges. Various techniques specific to RF circuits covering calibration trims, power management modules, co-existence issues, concurrent testing, and test measurements are explained. Results on different designs are presented. Together, they highlight the need and scope of adaptive test for RF circuits, and share a new dimension in the test of multi-core circuits, under different constraints of design, test and test equipment.