This paper presents the first experimental demonstration of fully Walsh-based wideband converters for Radio Frequency Front-End (RFFE) architecture implemented in 28 nm FDSOI CMOS technology. The system integrates a Walsh-based RF digital-to-analog converter (WDAC) and a Walsh-based RF analog-to-digital converter (WADC) designed for digital predistortion (DPD) applications. The WDAC achieves an instantaneous bandwidth of 4.7 GHz with an energy efficiency of 0.57 pJ/bit. The WADC achieves a 2.5 GHz bandwidth while consuming only 4 mW. The experimental demonstration showcase efficient wideband signal processing in the Walsh domain and highlights its potential as a promising paradigm for future low-power, high-throughput wireless transceivers, offering both wideband capabilities and a new order of scale energy efficiency.

Millimeter-wave bands open new bands of interest for ultra-high-speed wireless communications, but face technological bottlenecks such as power amplification. Working near the maximum oscillation frequency of the transistor leads to low intrinsic gain and low efficiency. Digital predistortion (DPD) achieves a higher power efficiency without degrading linearity. This paper presents a co-simulation workflow to design jointly DPD and the power amplifier (PA) in order to reach the optimum performances in linearity and power-added efficiency (PAE). The design of a PA at a center frequency of 142 GHz using the CMOS 28nm FDSOI technology from STMicroelectronics demonstrates how the design flow can take into account continuous wave (CW) and modulated signal simulations with and without DPD. The linearity performance is highlighted on the adjacent channel power ratio (ACPR) with an improvement of 4dB on an OFDM signal with 800 MHz bandwidth.

This paper investigates the use of Neural Network (NN) nonlinear modelling for Power Amplifier (PA) linearization in the Walsh-Hadamard transceiver architecture. This novel architecture has recently been proposed for ultra-high bandwidth systems to reduce the transceiver power consumption by extensive parallelization of the digital baseband hardware. The parallelization is achieved by replacing two-dimensional quadrature modulation with multi-dimensional Walsh-Hadamard modulation. The open research question for this architecture is whether conventional baseband signal processing algorithms can be similarly parallelized while retaining their performance. A key baseband algorithm, digital predistortion using NN models for PA linearization, will be adapted to the parallel Walsh architecture. A straighforward parallelization of the state-of-the-art NN architecture is extended with a cross-domain Knowledge Distillation pre-training method to achieve linearization performance on par with the quadrature implementation. This result paves the way for the entire baseband processing chain to be adapted into ultrahigh bandwidth, low-power Walsh transceivers.

This work details a new way to deal with the Multiple-Cell Upsets (MCU) in SRAM memories for space applications. The method consists of spatially interleaving a memory plan with a network of memory radiation detectors. As a proof-of-concept, a prototype circuit composed of the radiation detectors was manufactured in the 350 nm CMOS Process Technology and tested considering two methodologies: electrically-induced SEU/MCU testing and SEE laser testing. Silicon measurement results confirm the correct operation of the circuit, detecting single and multiple events inserted in different positions of the evaluated detection plans. According to the ratio between the number of data and detection cells, the method proposed can provide a probability of detecting MCUs in a memory plan that can reach close to 100%.

A novel non-linear adaptive filter for the linearization of Radio Frequency (RF) Power Amplifiers (PAs) is presented. In this study, we aim at reducing the Digital Predistortion (DPD) complexity and enhancing its convergence speed for reduced computation time. The Walsh Transform is used as a computational basis for evaluating a predistorter (PD) model. The mathematical properties of the Walsh theory are exploited to adapt a memory polynomial (MP) in the sequency domain. A block-based Walsh LMS is introduced to seek the optimal PD coefficients. Simulations and results of linearization of class-AB PAs are exhibited. The comparison with conventional DPD algorithms shows that the proposed method converges 10 times faster with a reduction of 12% of the complexity for similar accuracy. Finally, a complete DPD architecture based on the Walsh Transform is proposed.

The Latin American Symposium on Circuits and Systems (LASCAS) is the flagship conference in Latin America (IEEE Regions 9) by the IEEE Circuits and Systems Society (CASS). This conference contributes since more than a decade to its history by reporting the latest research results and innovations across the themes within the scope of the society, including analog, digital, and mixed-signal electronics, signal processing, power electronics, communication theory, sensors, circuit theory, and nonlinear circuits and systems.

This paper presents a very low-power 875 MS/s 7b single-channel high-speed successive approximation register (SAR) analog-to-digital converter (ADC) that achieves a SNDR/SFDR at Nyquist rate of 41.46/55.01 dB. The use of an integer-based split CDAC combined with an improvement for the LSB capacitor allows a substantial improvement in the SNDR. A simple and accurate calibration procedure for the ADC is presented thanks to body biasing. The ADC is designed in 22 nm FDSOI while consuming 1.65 mW from a 0.8 V supply with a core chip area of 0.00074 mm2. The Walden figure-of-merit of 19.5 fJ/conversion-step at Nyquist rate making it one of the lowest among recently published medium resolution SAR ADCs.

Word population has almost doubled during the last century increasing dramatically the need for food to support a population over 7 billion persons. According to FAO, by 2050, the agricultural production will have to increase by 70%. Plants growth depends on several factors such as nutrients (NPK), soil characteristics, soil Ph, soil moisture, temperature, weather, and light. To manage all the required information and the complexity of plants growth a system, based on IoT technology, able to measure, analyze, and act is needed. IoT is a solution for precision agriculture. A system for precision agriculture, that will be distributed in the field, far from energy and communication sources needs to be low power and able to process the received information and just sending the most relevant information to the cloud for further statistical analysis. This system will be able to measure the most important parameters for plant growth through a set of sensors and act to fix some of those parameters through actuators when needed as well.