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Handbook Of Modern Sensors Physics Designs And Applications

Sensors based on the detection of small resistance variations are universally recognized as piezoresistive. Being one of the simplest, most common and most investigated classes of sensors, continuous efforts are focused on creating improved devices with higher performance that can be used in many commercial and non–commercial applications (e.g. evaluation of strain, pressure, acceleration, force etc.). Consequently, despite the fact that more than 150 years have passed since the discovery of the piezoresistive effect in some classes of metals and semiconductors, the development of such sensors remains interesting and topical. Moreover, with the advent of second–generation robotics, research on piezoresistive sensors has undergone a massive increase. This paper aims to be a short, self–consistent vademecum which would be useful to researchers and engineers, since it focuses on the fundamentals of theory, materials, and readout–circuit design pertinent to the most recent developments in the field of piezoresistive sensors.

Theory, technology and applications of piezoresistive sensors: A review

The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins-a large array of sensors spatially distributed and capable of rapid somatosensory perception-will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)-a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)-enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

A neuro-inspired artificial peripheral nervous system for scalable electronic skins.

With the prospect of a smart society in the foreseeable future, humans are experiencing an increased link to electronics in the digital world, which can benefit our life and productivity drastically. In recent decades, advances in the Human Machine Interface (HMI) have improved from tactile sensors, such as touchpads and joysticks, to now include the accurate detection of dexterous body movements in more diversified and sophisticated devices. Advancements in highly adaptive machine learning techniques, neural interfaces, and neuromorphic sensing have generated the potential for an economic and feasible solution for next-generation applications such as wearable HMIs with intimate and multi-directional sensing capabilities. This review offers a general knowledge of HMI technologies beginning with tactile sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on how machine learning, neural interfaces, and neuromorphic electronics can be used to enhance next-generation HMIs in an upcoming 5 G infrastructure and advancements in the internet of things and artificial intelligence of things in the near future. The efficient interactions with kinetic and physiological signals from human body through the fusion of tactile sensor and neural electronics will bring a revolution to both the advanced manipulation and medical rehabilitation.

Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems

With the rapid development of intelligent technology, tactile sensors as sensing devices constitute the core foundation of intelligent systems. Biological organs that can sense various stimuli play vital roles in the interaction between human beings and the external environment. Inspired by this fact, research on skin-like tactile sensors with multifunctionality and high performance has attracted extensive attention. An overview of the development of high-performance tactile sensors applied in intelligent systems is systematically presented. First, the development of tactile sensors endowed with stretchability, self-healing, biodegradability, high resolution and self-powered capability is discussed. Then, for intelligent systems, tactile sensors with excellent application prospects in many fields, such as wearable devices, medical treatment, artificial limbs and robotics, are presented. Finally, the future prospects of tactile sensors for intelligent systems are discussed.

Recent progress in tactile sensors and their applications in intelligent systems

Tactile sensors are basically arrays of force sensors that are intended to emulate the skin in applications such as assistive robotics. Local electronics are usually implemented to reduce errors and interference caused by long wires. Realizations based on standard microcontrollers, Programmable Systems on Chip (PSoCs) and Field Programmable Gate Arrays (FPGAs) have been proposed by the authors for the case of piezoresistive tactile sensors. The solution employing FPGAs is especially relevant since their performance is closer to that of Application Specific Integrated Circuits (ASICs) than that of the other devices. This paper presents an implementation of such an idea for a specific sensor. For the purpose of comparison, the circuitry based on the other devices is also made for the same sensor. This paper discusses the implementation issues, provides details regarding the design of the hardware based on the three devices and compares them.

Three realizations and comparison of hardware for piezoresistive tactile sensors.

Dexterity in robotic hands is not enough to cover the demands for more flexibility in the manufacturing process. Specifically, processes that involve cooperation or learning from humans, slippage detection and tactile feedback to improve grasp stability when different objects are manipulated, object exploration and classification, quality inspection, welding, etc. The sense of touch is the key to the capabilities of human hands, so tactile sensors must be incorporated into robotic hands to improve them. However, tactile sensing technology is not yet mature enough to be an effective tool. One reason for this limited performance is the need of acquisition and processing of large amounts of data in the short time required to perform a fluent manipulation. Smart tactile sensors can help in this goal if they are able to carry out local acquisition and processing of tactile data and transmit only relevant information through buses that guarantee bounded latency. Field-programmable gate arrays (FPGAs) are devices which are especially suitable for this task due to their ability for parallel processing. This paper presents the realization of electronics for a tactile sensor suite based on FPGAs that implements a direct interface with the raw sensor and serial communication between the fingertips and palm.

FPGA-Based Tactile Sensor Suite Electronics for Real-Time Embedded Processing

The typical layout in a piezoresistive tactile sensor arranges individual sensors to form an array with M rows and N columns. While this layout reduces the wiring involved, it does not allow the values of the sensor resistors to be measured individually due to the appearance of crosstalk caused by the nonidealities of the array reading circuits. In this paper, two reading methods that minimize errors resulting from this phenomenon are assessed by designing an electronic system for array reading, and the results are compared to those obtained using the traditional method, obviating the nonidealities of the reading circuit. The different models were compared by testing the system with an array of discrete resistors. The system was later connected to a tactile sensor with 8 × 7 taxels.

https://www.mdpi.com/1424-8220/17/11/2513/pdf

High-Accuracy Readout Electronics for Piezoresistive Tactile Sensors.

Resistive sensor arrays are formed by a large number of individual sensors which are distributed in different ways. This paper proposes a direct connection between an FPGA and a resistive array distributed in M rows and N columns, without the need of analog-to-digital converters to obtain resistance values in the sensor and where the conditioning circuit is reduced to the use of a capacitor in each of the columns of the matrix. The circuit allows parallel measurements of the N resistors which form each of the rows of the array, eliminating the resistive crosstalk which is typical of these circuits. This is achieved by an addressing technique which does not require external elements to the FPGA. Although the typical resistive crosstalk between resistors which are measured simultaneously is eliminated, other elements that have an impact on the measurement of discharge times appear in the proposed architecture and, therefore, affect the uncertainty in resistance value measurements; these elements need to be studied. Finally, the performance of different calibration techniques is assessed experimentally on a discrete resistor array, obtaining for a new model of calibration, a maximum relative error of 0.066% in a range of resistor values which correspond to a tactile sensor.

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Accuracy and Resolution Analysis of a Direct Resistive Sensor Array to FPGA Interface

One of the most suitable ways of distributing a resistive sensor array for reading is an array with M rows and N columns. This allows reduced wiring and a certain degree of parallelism in the implementation, although it also introduces crosstalk effects. Several types of circuits can carry out the analogue-digital conversion of this type of sensors. This article focuses on the use of operational amplifiers with capacitive feedback and FPGAs for this task. Specifically, modifications of a previously reported circuit are proposed to reduce the errors due to the non-idealities of the amplifiers and the I/O drivers of the FPGA. Moreover, calibration algorithms are derived from the analysis of the proposed circuitry to reduce the crosstalk error and improve the accuracy. Finally, the performances of the proposals is evaluated experimentally on an array of resistors and for different ranges.

José A. Sánchez-Durán

Papers

Three realizations and comparison of hardware for piezoresistive tactile sensors.

FPGA-Based Tactile Sensor Suite Electronics for Real-Time Embedded Processing

High-Accuracy Readout Electronics for Piezoresistive Tactile Sensors.

Accuracy and Resolution Analysis of a Direct Resistive Sensor Array to FPGA Interface

Improved Circuits with Capacitive Feedback for Readout Resistive Sensor Arrays.