Mixed-signal Components Research Articles

Intelligent Identification of Trend Components in Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time series analysis. It allows solving various problems related to time series without the need to define a model. In this study, we focus on the problem of trend extraction. To extract trends using SSA, a grouping of elementary components is required. However, automating this process is challenging due to the nonparametric nature of SSA. Although there are some known approaches to automated grouping in SSA, they do not work well when the signal components are mixed. In this paper, a novel approach that combines automated identification of trend components with separability improvement is proposed. We also consider a new method called EOSSA for separability improvement, along with other known methods. The automated modifications are numerically compared and applied to real-life time series. The proposed approach demonstrated its advantage in extracting trends when dealing with mixed signal components. The separability-improving method EOSSA proved to be the most accurate when the signal rank is properly detected or slightly exceeded. The automated SSA was very successfully applied to US Unemployment data to separate an annual trend from seasonal effects. The proposed approach has shown its capability to automatically extract trends without the need to determine their parametric form.

Interpretable machine learning in VLSI physical design

Today's popularisation of portable devices largely depends on the progress in integrated circuits. Modern Very Large Scale Integration technology (VLSI) allows billions of transistors to be packed into the same chip. In the past years, digital design in VLSI has been developed compared to analogue design. The traditional method is hard to model the performance change in analogue or mixed-signal components caused by physical design. In the early 2000s, rapid advances in machine learning and computing power made analogue design automation possible. Despite their outstanding performance, the transparency issue has become significant. This paper introduces the history of VLSI physical design, which includes placement and routing in the early stages. The change that machine learning (ML) has made is mentioned in the third section. Analysis of the potential problem has been proposed, followed by a brief category of some well-known work in interpretable Machine Learning, which could be the primary direction for VLSI automation to be further popularised in the future.

Digital Fault-based Built-in Self-test and Evaluation of Low Dropout Voltage Regulators

With increasing pressure to obtain near-zero defect rates, there is a need to explore built-in self-test and other non-traditional test techniques for embedded mixed-signal components, such as PLLs, power converters, and data converters. This article presents an extremely low-cost built-in self-test technique for LDOs, specifically designed for fault detection. The methodology relies on exciting the LDO loop at the voltage reference input via a pseudo-random signal with white noise characteristics and observing the response from the output of LDO via all-digital circuitry, thereby inducing low area and performance overhead. The BIST circuit along with an LDO as a device under test is designed in 65nm technology. Fault simulations performed at the transistor level show that all resistive open/short defects in circuit components can be detected even if they do not cause a catastrophic failure in the LDO response. The proposed technique is validated with hardware using off-the-shelf components.

Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

Investigation of DC, RF and Linearity Performances of III–V Semiconductor-Based Electrically Doped TFET for Mixed Signal Applications

The aim of this paper is to bring forward a novel hetero-material electrically doped (ED) GAA TFET for high-performing and power efficient mixed signal applications. A number of low and high band gap III–V semiconductors were considered at source and drain channel regions, and their electrical characteristics were compared to identify the best alternative. To that end, DC/RF/linearity properties of AlGaSb/GaAsP, Ge/GaAs, Si/GaAs, Si/Ge, Silicon and SiGe/Si based ED GAA TFETs were analyzed. We found that the AlGaSb/GaAs-ED-TFET provides 16 $$\mu \hbox {A}$$ ON current at 25 mV/decade subthreshold swing and 1e13 $$I_{ON}/ I_{OFF}$$ ratio with exceptional analog and linearity characteristics. Also, we found that interface trap charges (ITC) in AlGaSb/GaAs-ED-TFET present negligible impact and have no effect on system performance. Further, improvisation is possible through mole fraction optimization of $$Al_{x}Ga_{1-x}Sb$$ . Finally, the mixed signal components that are widely available in system architectures are implemented using AlGaSb/GaAs-ED-TFET and their performance criteria are measured. All device simulations were performed using the TCAD Silvaco tool, and look-up based Verilog-A technique was used for circuit simulation in Cadence.

Fault-based Built-in Self-test and Evaluation of Phase Locked Loops

With the increasing pressure to obtain near-zero defect rates for the automotive industry, there is a need to explore built-in self-test and other non-traditional test techniques for embedded mixed-signal components, such as PLLs, DC-DC converters, and data converters. This article presents a very low-cost built-in self-test technique for PLLs specifically designed for fault detection. The methodology relies on exciting the PLL loop in one location via a pseudo-random signal with noise characteristics and observing the response from another location in the loop via all digital circuitry, thereby inducing low area and performance overhead. The BIST circuit along with a PLL under test is designed in 65 nm technology. Fault simulations performed at the transistor and system-level show that the majority of non-catastrophic faults that result in parametric failures can be detected with the proposed approach.

Assertions for Protecting Mixed-Signal Latency Contracts in Power Management

Mixed-signal components, such as low dropouts (LDOs) and phase locked loops (PLLs), are widely used inside the on-chip power management fabric of low power integrated system-on-chip (SoC) designs. The digital brain of the power management logic that is responsible for regulating the power delivery to different power domains in the chip has to consider the real time latencies of the analog components, which otherwise leads to functional errors in the domains being driven. The latencies may be viewed as contracts between the digital and the analog. This article presents an approach for generating assertions for protecting such mixed-signal latency contracts and using them to rule out timing bugs in the power management logic. Our tool flow enables the verification of the power management fabric, combining a novel mixed-signal assertion checking method in a simulation setting, and a full formal verification method for the digital brain of the power management logic. To the best of our knowledge, this is the first framework where assertions are used for binding analog latency contracts on the digital logic of power management.

Mixed-Signal SoC Characterization for Future 5G DPD Applications [Application Notes

RF and microwave system design is a complex process that often requires several iterations of subsystem design. An example is a mixed-signal component (where analog and digital waveforms coexist) composed of several subsystems, with the most important being the sampling circuit and quantization subsystem, possibly extended with RF up- or downconversion. Analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) are mixed-signal components used in softwaredefined radio (SDR) front ends. Understanding the behavior of these mixed-signal components is important when it is necessary to connect them to other components in building a full RF system.

Leaky Noise: New Side-Channel Attack Vectors in Mixed-Signal IoT Devices

Microcontrollers and SoC devices have widely been used in Internet of Things applications. This also brings the question whether they lead to new security threats unseen in traditional computing systems. In fact, almost all modern SoC chips, particularly in the IoT domain, contain both analog and digital components, for various sensing and transmission tasks. Traditional remote-accessible online systems do not have this property, which can potentially become a security vulnerability. In this paper we demonstrate that such mixed-signal components, namely ADCs, expose a new security threat that allows attackers with ADC access to deduce the activity of a CPU in the system. To prove the leakage, we perform leakage assessment on three individual microcontrollers from two different vendors with various ADC settings. After showing a correlation of CPU activity with ADC noise, we continue with a leakage assessment of modular exponentiation and AES. It is shown that for all of these devices, leakage occurs for at least one algorithm and configuration of the ADC. Finally, we show a full key recovery attack on AES that works despite of the limited ADC sampling rate. These results imply that even remotely accessible microcontroller systems should be equipped with proper countermeasures against power analysis attacks, or restrict access to ADC data.

On Analog QAM Demodulation for Millimeter-Wave Communications

Recent interest in wideband multi-giga-bit-per-second wireless communications over millimeter-wave frequencies has created both new opportunities and design challenges. The realization of such technologies including multi-giga-samples-per-second data conversion and digital signal processing systems is extremely challenging. In this brief, we propose a fully analog QAM demodulator as a step toward eliminating the power-hungry and ultra-high speed mixed-signal components (e.g., analog-to-digital converter). The proposed low-complexity, low-overhead solution is shown to be robust against analog processing errors.

Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes is that they have high computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable ofminimizing the bit error ratio (BER) and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.

Alias-Free Products of Signals Near Nyquist Rate

Products of time-series signals have found wide-spread application in many fields of signal processing. For example, they are often used in modeling and compensating distortions generated by analog and mixed-signal components. Without excess bandwidth, the products of time-series signals will produce aliased artifacts that do not represent the physical phenomenology of the components being modeled. To ameliorate the effects of these potentially unwanted, aliased distortion products, we compare two multidimensional filters: the first is a simple one-dimensional (1-D) polyphase filter that is used to generate excess bandwidth; the second is a multidimensional (M-D) filter that is convolved with the time-series product. We demonstrate that the 1-D polyphase filter, although computationally simple, still produces unwanted aliasing when applied to signals near Nyquist rate, which can adversely affect modeling and/or compensation performance. By contrast, the M-D filter is computationally expensive, but is capable of most closely matching the ideal antialias filter response.

Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to avoid energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully-connected architecture requiring a large number of phase shifters, which is also energyintensive. In this paper, we focus on the more energy-efficient hybrid precoding with sub-connected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multi-user signal detection, we first propose to decompose the total achievable rate optimization problem with non-convex constraints into a series of simple sub-rate optimization problems, each of which only considers one sub-antenna array. Then, we prove that maximizing the achievable sub-rate of each sub-antenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SICbased hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify the near-optimal performance of SIC-based hybrid precoding.

Design Flow of a Digital IC: The role of digital IC\/SOC design in CE products

The Central Module of Consumer Electronics (CE) products is a miniature-sized IC that finds wide-spectrum applicability from kitchen appliances to automobiles, aircraft, or any embedded systems. The system in these modern CE products is built as an analog/mixed-signal SOC (AMS-SOC). In a typical case, the digital circuits are the main computational modules, whereas the analog or mixed-signal components are interfacing circuits. Proficient design of digital ICs has become more important because it is one of the significant driving factors of efficient system design in this current mobile electronics era. The various factors involved during IC design, such as speed, power, and reliability, are shown in Figure 1.

Finite element modeling and identification of metallic materials step responses

Purpose – The purpose of this paper is to study the optimization of a pulsed-excitation gradiometric inductive sensing system. Design/methodology/approach – The authors applied numerical finite-element modeling for the simulation of the step responses of different target materials to identify the particular contribution of the magnetic permeability and the electric conductivity. Four materials of technical importance (aluminum, copper, constructional steel and stainless steel) and four fictive test materials were modeled for the comparison of different materials possessing a wide range of combinations of material parameters. A microcontroller-based measurement setup was implemented for the qualitative validation of the simulation results. A simple signal processing chain was also applied for the time-domain conversion of the direct step response signals to increase the time scale of the signals to be processed by common mixed-signal components. Findings – The step response signals contain relevant information of the target material quality and the sensor-to-target distance. The target materials can be distinguished and the sensor-target distance can be determined by the evaluation of the step response signals with an appropriate algorithm based on the measurement of the time and voltage of an extreme of the time dependent measurement signals. Both direct and time-domain converted signals can be used for material independent proximity sensing. Originality/value – In order to design an inductive proximity switch, an evaluation method of the response signals has been proposed by using an analog RLC circuit. With the presented method, a target material invariant inductive proximity switch can be realized.

Time-Reversal Imaging Using One Transmitting Antenna Based on Independent Component Analysis

Time-reversal (TR) techniques have shown promising results for detection and localization of scatterers in multiple scattering environments. These techniques exploit the TR invariance of the wave equation. Most of TR applications require the analysis of the TR operator obtained from the MDM of a TR array. Eigenvalue decomposition of the TR operator forms the basis of techniques such as decomposition of the TR operator and TR multiple signal classification. In this letter, we have introduced a new approach that is based on the independent component analysis (ICA). The ICA takes advantage of the statistical independence of scattered signals from well-resolved scatterers and breaks the mixed received signal by each receiving antenna to independent components based on maximizing the non-Gaussianity of mixed signal components. Some preprocessing steps such as centering and whitening are also required. One advantage of the TR imaging based on ICA is that imaging and focusing can be achieved using only one transmitting antenna, which lowers the complexity of the implementation compared with other techniques.

A New Optimum Power Control Scheme for Low-Power Energy Harvesting Systems

Energy harvesting has become a popular source for low-power electronic systems such as wireless sensors and biomedical implants. Energy can be extracted from a number of ambient conditions such as vibration, solar, and thermal gradient. Just like renewable sources, the associated switching power converters can be controlled to harvest maximum power from these miniature systems. However, conventional schemes, such as a conventional maximum power point tracking (MPPT) controller for solar cells, are complex and cannot be utilized in low-power energy systems due to their cost and power requirements. In this paper, a novel optimum energy harvesting scheme is proposed, which controls the duty cycle of the converter to maximize the output power of the system. The control system employs only simple mixed-signal components and can be applied to low-power systems. The proposed scheme does not depend on the characteristics of a specific source and is applicable for different energy systems. In this paper, it is utilized to achieve optimized energy harvesting for two completely different fixed excitation systems, namely, a low-voltage vibration-based electromagnetic microgenerator and a miniature solar cell array. Both simulation and experimental results are provided to validate the proposed scheme.

Chassis

Power Management Units (PMUs) are large integrated circuits consisting of many predesigned mixed-signal components. PMU integration poses a serious verification problem considering the size of the integrated circuit and the complexity of analog simulation. In this article we present an approach for automatic generation of behavioral models for PMU components from top-down skeleton models, fitted with parameter values estimated by bottom-up parameter extraction algorithms. It is shown that replacing PMU components with these autogenerated hybrid automata-based abstract behavioral models enables significant simulation speedup (> 20X on our industrial test cases) and helps in early detection of integration errors. The article also justifies the level of accuracy in our models with respect to the goal of verifying integrated PMUs. The approach presented in this work is implemented in the form of a tool suite called Chassis.

Fast Digital Post-Processing Technique for Integral Nonlinearity Correction of Analog-to-Digital Converters: Validation on a 12-Bit Folding-and-Interpolating Analog-to-Digital Converter

The semiconductor industry tends to constantly increase the performances of developed systems with an ever-shorter time-to-market. In this context, the conventional strategy for mixed-signal component design, which is based only on analog design effort, will no longer be suitable. In this paper, a digital correction technique is presented for analog-to-digital converters (ADCs). The idea is to use a lookup table (LUT) for the online correction of integral nonlinearity (INL). The main challenge for this kind of technique is the cost in time and resources to estimate the actual INL of the ADC needed to load the LUT. In this paper, we propose to extract INL with a very rapid procedure based on spectral analysis. We validate our technique on a 12-bit folding-and-interpolating ADC and we demonstrate that the correction is efficient for a large range of application fields.

An Analog-Node Model for VHDL-Based Simulation of RF Integrated Circuits

This paper describes a very-high-speed integrated-circuit hardware description language (VHDL)-based analog-node model, an associated driver component for the mixed-signal event-driven (MixED) simulation technique, and some primitive device models applied to radio-frequency integrated circuits. With the presented MixED method, analog circuits are modeled as a composition of controlled sources. Unlike other VHDL-based analog simulation methods, these MixED sources compute not only a real number representing an output voltage but also an output impedance. This allows the outputs of several MiXED sources to be connected in order to drive the same node signal <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . The voltage of this record-type signal is automatically computed at its element <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</i> by resolution functions in compliance with Kirchhoff's current law. The data structure of the node signal <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , its self-defined resolution functions, and an associated driver component are presented and discussed to meet different simulation requirements, such as speed, versatility, current accuracy, and adaptive time stepping. Several examples demonstrate how to behaviorally model mixed-signal components with this method with an emphasis on the simulation of a heterodyne receiver. Simulation speeds are compared to VHDL-AMS tools.