Search

The multi‐stage noise shaping (MASH) ΣΔ ADC has a good potential to achieve high‐order noise shaping (NS) and high resolution. However, it suffers from quantization noise leakage caused by the mismatch between the analogue NS filter and the digital cancellation filter, which greatly degrades the ADC performance. The sturdy MASH topology of the ΣΔ ADC can solve the leakage issue, but it cannot be implemented using the NS SAR ADC due to structural limitations. This paper proposes a sturdy MASH 2‐0 NS SAR to solve the noise leakage issue. The 4th‐order NS is achieved by only using a 2‐0 topology, which is hardware efficient. Instead of eliminating the first‐stage quantization error, the proposed sturdy MASH 2‐0 NS SAR shapes it, achieving better robustness to PVT variables. Furthermore, owing to the first‐stage 2nd‐order NS capability, the impairments of the residue amplifier, including the gain error and nonlinearity, are 2nd‐order shaped. The proposed 4th‐order sturdy MASH 2‐0 NS SAR is implemented in a 28 nm CMOS process, which achieves a SNDR of 85.6 dB and a SFDR of 101.3 dB with 10 MHz BW at OSR of 10, resulting in a Schreier FoM of 178.8 dB/179.4 dB (in SNDR/DR) with power consumption of 4.8 mW. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the effects of error-feedback noise shaping (EFNS) on a low-resolution Kramers-Kronig (KK) detection system for the first time. Both 16-ary quadrature amplitude modulation (16-QAM) and 64-ary quadrature amplitude modulation (64-QAM) were considered in the experiment. The carrier tone was added on the transmitter side using the virtual carrier-assisted single sideband (VCA-SSB) method to meet the minimum phase (MP) condition. Under the MP condition, KK detection only needs the intensity of the optical signal to be detected by one photodiode (PD) to retrieve the phase information. However, the virtual carrier tone increases the carrier-to-signal power ratio (CSPR), which results in a high peak-to-average power ratio (PAPR) at the transmitter, causing an increase in quantization noise. To mitigate quantization noise in the low-resolution KK detection system, EFNS was applied. The application of EFNS increased the optimal CSPR of the KK detection system and improved receiver sensitivity. Using a 4-bit DAC and the EFNS, 80 km transmission was achieved in the 60-Gbit/s 64-QAM KK detection system. [ABSTRACT FROM AUTHOR]

Context. Microwave stub filters are widely used in radio engineering and telecommunication systems, as well as in technical information protection systems due to simplicity of design, possibility of realization in microstrip design and manufacturability in mass production. For synthesis of stub filters nowadays traditional methods based on transformation of low-frequency prototype filters on LC-elements into filtering structures on elements with distributed parameters are used. The transformations used are approximate and provide satisfactory results for narrowband stub filters. In this connection there is a necessity in development of direct synthesis methods for stub filters, excluding various approximations and providing obtaining of amplitude-frequency characteristics with optimal shape for any bandwidths. Objective. The purpose of the study is to develop a method for direct synthesis of stub band-pass filters and low-pass filters with Chebyshev amplitude-frequency response in the passband. Method. The procedure of direct synthesis includes the formulation of relations for filter functions of plume structures, selection of approximating functions of Chebyshev type for filter functions and formation of a system of nonlinear equations for calculation of parameters of filter elements. Results. A method for the direct synthesis of stub bandpass and lowpass filters with Chebyshev response is developed. Conclusions. Scientific novelty of the work consists in the development of a new method of direct synthesis of l stub filters. The method, in contrast to approximate traditional methods of synthesis of microwave filters, is exact, and the obtained solutions of synthesis problems are optimal. The experiments confirmed the performance of the proposed method and the optimality of the obtained solutions. Prospects for further research suggest adapting the method to the synthesis of filter structures with more complex resonators compared to stubs. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. Memristors hold promise for massively-parallel computing at low power. Aguirre et al. provide a comprehensive protocol of the materials and methods for designing memristive artificial neural networks with the detailed working principles of each building block and the tools for performance evaluation. [ABSTRACT FROM AUTHOR]