Wide input range, fully-differential indirect current feedback instrumentation amplifier for self-x sensory systems / Symmetrischer Instrumentierungsverstärker mit indirekter Stromgegenkopplung und hoher Eingangsignalspanne für integrierte Sensorsysteme mit Self-x-Eigenschaften

König

2021

J. Sens. Sens. Syst.

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Abstract. This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.

Section: Indirect Current-feedback Instrumentation Amplifier (Cfia)mentioning

confidence: 89%

Section: Transistor Level Design Of Reconfigurable Cfiamentioning

confidence: 99%

Efficient transient testing procedure using a novel experience replay particle swarm optimizer for THD-based robust design and optimization of self-X sensory electronics in industry 4.0

König

2021

J. Sens. Sens. Syst.

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“…This limitation becomes particularly problematic when interfacing high dynamic range sensors, such as magnetoresistive sensors [51]. In order to address this issue, the authors in [52] proposed a wide-inputrange fully differential CFIA solution based on a fully balanced DDF [53]. This method enabled the concurrent achievement of outstanding dynamic performance and wide-inputrange capabilities.…”

Section: Indirect Current-feedback Instrumentation Amplifier (Cfia)mentioning

confidence: 99%

Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

König

2023

Chips

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This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase.

“…An innovative solution to this problem was proposed in [152], which uses the advantages of fully differential signal properties and the method to apply the negative feedback to the input and feedback transconductance stages of the DDF to create a virtual short between each pair. We proposed in [153] a fully differential CFIA based on the last solution. The reported post-layout simulations remarked on a high dynamic performance to process a large differential voltage of 1.6 V p−p and achieved a total harmonics distortion (THD) of 0.95% at a signal frequency of 5 MHz, while consuming only 2.55 mW and a layout area of 0.039 mm 2 .…”

Section: Instrumentation Amplifiermentioning

confidence: 99%

Integrated Sensor Electronic Front-Ends with Self-X Capabilities

Abd

et al. 2022

Chips

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The ongoing vivid advance in integration technologies is giving leverage both to computing systems as well as to sensors and sensor systems. Both conventional computing systems as well as innovative computing systems, e.g., following bio-inspiration from nervous systems or neural networks, require efficient interfacing to an increasing diversity of sensors under the constraints of metrology. The realization of sufficiently accurate, robust, and flexible analog front-ends (AFE) is decisive for the overall application system and quality and requires substantial design expertise both for cells in System-on-Chip (SoC) or chips in System-in-Package (SiP) realizations. Adding robustness and flexibility to sensory systems, e.g., for Industry 4.0., by self-X or self-* features, e.g., self-monitoring, -trimming, or -healing (AFEX) approaches the capabilities met in living beings and is pursued in our research. This paper summarizes on two chips, denoted as Universal-Sensor-Interface-with-self-X-properties (USIX) based on amplitude representation and reports on recently identified challenges and corresponding advanced solutions, e.g., on circuit assessment as well as observer robustness for classic amplitude-based AFE, and transition activities to spike domain representation spiking-analog-front-ends with self-X properties (SAFEX) based on adaptive spiking electronics as the next evolutionary step in AFE development. Key cells for AFEX and SAFEX have been designed in XFAB xh035 CMOS technology and have been subject to extrinsic optimization and/or adaptation. The submitted chip features 62,921 transistors, a total area of 10.89 mm2 (74% analog, 26% digital), and 66 bytes of the configuration memory. The prepared demonstrator will allow intrinsic optimization and/or adaptation for the developed technology agnostic concepts and chip instances. In future work, confirmed cells will be moved to complete versatile and robust AFEs, which can serve both for conventional as well as innovative computing systems, e.g., spiking neurocomputers, as well as to leading-edge technologies to serve in SOCs.