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Review
. 2023 Mar 28;17(6):5211-5295.
doi: 10.1021/acsnano.2c12606. Epub 2023 Mar 9.

Technology Roadmap for Flexible Sensors

Yifei Luo  1   2 Mohammad Reza Abidian  3 Jong-Hyun Ahn  4 Deji Akinwande  5   6 Anne M Andrews  7 Markus Antonietti  8 Zhenan Bao  9 Magnus Berggren  10   11 Christopher A Berkey  12 Christopher John Bettinger  13 Jun Chen  14 Peng Chen  15 Wenlong Cheng  16   17 Xu Cheng  18 Seon-Jin Choi  19 Alex Chortos  20 Canan Dagdeviren  21 Reinhold H Dauskardt  12 Chong-An Di  22 Michael D Dickey  23 Xiangfeng Duan  24 Antonio Facchetti  25 Zhiyong Fan  26 Yin Fang  15 Jianyou Feng  27 Xue Feng  28 Huajian Gao  29   30 Wei Gao  31 Xiwen Gong  32 Chuan Fei Guo  33 Xiaojun Guo  34 Martin C Hartel  14 Zihan He  22 John S Ho  35   36   37 Youfan Hu  38 Qiyao Huang  39 Yu Huang  40 Fengwei Huo  41 Muhammad M Hussain  42 Ali Javey  43   44 Unyong Jeong  45 Chen Jiang  46 Xingyu Jiang  47 Jiheong Kang  48 Daniil Karnaushenko  49 Ali Khademhosseini  50 Dae-Hyeong Kim  51 Il-Doo Kim  52 Dmitry Kireev  5   6 Lingxuan Kong  15 Chengkuo Lee  36   53   54   55 Nae-Eung Lee  56 Pooi See Lee  57   58 Tae-Woo Lee  59   60   61   62 Fengyu Li  63 Jinxing Li  64 Cuiyuan Liang  65 Chwee Teck Lim  66   67   68 Yuanjing Lin  69 Darren J Lipomi  70 Jia Liu  71 Kai Liu  72 Nan Liu  73 Ren Liu  71 Yuxin Liu  1   74 Yuxuan Liu  75 Zhiyuan Liu  76 Zhuangjian Liu  30 Xian Jun Loh  1 Nanshu Lu  77 Zhisheng Lv  1 Shlomo Magdassi  78 George G Malliaras  79 Naoji Matsuhisa  80 Arokia Nathan  81 Simiao Niu  82 Jieming Pan  36 Changhyun Pang  83 Qibing Pei  84 Huisheng Peng  27 Dianpeng Qi  65 Huaying Ren  85 John A Rogers  86   87 Aaron Rowe  88   89 Oliver G Schmidt  49   90   91 Tsuyoshi Sekitani  92 Dae-Gyo Seo  59 Guozhen Shen  93 Xing Sheng  94 Qiongfeng Shi  36   53   54 Takao Someya  95 Yanlin Song  96 Eleni Stavrinidou  97 Meng Su  96 Xuemei Sun  27 Kuniharu Takei  98 Xiao-Ming Tao  99 Benjamin C K Tee  100   101 Aaron Voon-Yew Thean  36   102 Tran Quang Trung  56 Changjin Wan  103 Huiliang Wang  104 Joseph Wang  105 Ming Wang  106   107 Sihong Wang  108 Ting Wang  109 Zhong Lin Wang  110   111 Paul S Weiss  112 Hanqi Wen  15   113 Sheng Xu  114 Tailin Xu  115 Hongping Yan  9 Xuzhou Yan  72 Hui Yang  116 Le Yang  1   117 Shuaijian Yang  118 Lan Yin  119 Cunjiang Yu  120 Guihua Yu  121 Jing Yu  57 Shu-Hong Yu  122 Xinge Yu  123 Evgeny Zamburg  36   102 Haixia Zhang  124 Xiangyu Zhang  36   102 Xiaosheng Zhang  125 Xueji Zhang  126 Yihui Zhang  127 Yu Zhang  36   102 Siyuan Zhao  71 Xuanhe Zhao  128 Yuanjin Zheng  129 Yu-Qing Zheng  130 Zijian Zheng  131 Tao Zhou  132 Bowen Zhu  133 Ming Zhu  134 Rong Zhu  135 Yangzhi Zhu  136 Yong Zhu  137 Guijin Zou  30 Xiaodong Chen  1   138
Affiliations
Review

Technology Roadmap for Flexible Sensors

Yifei Luo et al. ACS Nano. .

Abstract

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Keywords: bioelectronics; body area sensor networks; conformable sensors; flexible electronics; human-machine interfaces; mechanics engineering; soft materials; sustainable electronics; technology translation.

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Figures

Figure 1.
Figure 1.
Evolution of sensor technology. Sensors 1.0 only served the function of detection. Sensors 2.0 had the hallmark of electrical feedback. The first transistors achieved sensing as well as the amplification of current., Sensors 3.0 are characterized by miniaturization and integration. A smartphone is equipped with pressure sensors, light sensors, sound sensors, temperature sensors, image sensors, motion sensors, location sensors, among many others; smartwatches are likewise equipped with an increasing cohort of diverse sensors. Budding Sensors 4.0 technologies involve sensor networks and advanced algorithms to achieve enhanced perception capabilities and intimate cooperation between humans and machines.
Figure 2.
Figure 2.
An overview of issues covered in this Review, including challenges for flexible sensors in the near future (left) and issues to address in the long run (right). The five aspects of challenges are interrelated, reflected by the tan lines in the background.
Figure 3.
Figure 3.
Overview of key issues in the sensing performance of flexible sensors.
Figure 4.
Figure 4.
Barrier properties against (a) water and (b) oxygen of common materials used in flexible sensors, plotted versus elastic modulus. Softer materials are more flexible and usually more stretchable but suffer from poorer barrier properties. Soft barrier materials with moduli comparable to elastomers and hydrogels, as well as permeabilities comparable to inorganics are highly desirable for effective encapsulation (red arrows). Adapted with permission from ref . Copyright 2018 American Chemical Society.
Figure 5.
Figure 5.
Selective gas sensors. (a) Overview of the strategies to achieve selective gas sensing, including specific sensing materials and selective sensing arrays. Flexible gas sensors target applications in health monitoring and point-of-care diagnostics. AI, artificial intelligence. (b) Examples of metal–organic framework-based nanomaterials with high sensing specificity to gas molecules. Cu3-(HHTP)2 (HHTP=2,3,6,7,10,11-hexahydroxytriphenylene) thin film for room-temperature NH3 detection. Adapted with permission from ref . Copyright 2017 Wiley-VCH Verlag GmbH &Co. KGaA, Weinheim. Bimetallic nanoparticles (PtRu) confined in two-dimensional Cu3-(HHTP)2 for NO2 detection. Adapted with permission from ref . Copyright 2021 Wiley-VCH GmbH. Zn-based zeolite imidazole framework (ZIF-8) as molecular sieving layer for selective H2 filtration on Pd nanowires. Adapted with permission from ref . Copyright 2017 American Chemical Society. (c) An example of nanomaterial-based selective sensing array for volatile organic compound (VOC) identification. A graphene-based stretchable chemiresistive sensor array for identification of 13 plant VOCs. Reproduced with permission from ref . Copyright 2021 Elsevier.
Figure 6.
Figure 6.
Current interconnection approaches for flexible sensors and arrays. (a) Extended flexible sensor system layout contains flexible cable and pins suitable for a flat flexible cable (FFC) connector. (b) External FFC is bonded to the flexible sensor system via an anisotropic conductive film (ACF). (c) Printed conductors connect flexible sensor system with external electronics directly.
Figure 7.
Figure 7.
Performance of intrinsically stretchable (a) conductors and (b) semiconductors. (a) Intrinsically stretchable conductors are realized in three materials categories: nanocomposites, conducting polymers, and liquid metals; ionic conductors are not included. Conductivities of stretched liquid metals are calculated from resistance changes under unidirectional strain, based on the assumption of incompressible solid undergoing uniform deformation. Non-stretchable bulk metals are plotted in the gray band for comparison. Values extracted from refs , , -. (b) Stretchable semiconductors are based on semiconducting CNTs or conjugated polymers. Mobility measurement was done in either stretched materials transferred to non-stretchable substrates (channel only) or fully stretchable transistors and transistor arrays (full transistor). Values for stretch directions both parallel and perpendicular to the channel length are included. Typical mobilities of common non-stretchable semiconductors are plotted in bands for comparison. Red arrow indicates area for improvement. Values extracted from refs , , -.
Figure 8.
Figure 8.
Conventional time division multiple access (blue panel) and emerging event-driven spike generation (red panel) as array data collection strategies. (a,b) Passive-matrix and active-matrix designs are the most commonly used array readout strategies. (c) Biological somatosensory system uses spike trains to encode tactile information with high spatiotemporal resolution and energy efficiency. (d) Artificial sensory systems can mimic the structure and function of the biological somatosensory system. Each sensing pixel generates potential spikes and is thus called an artificial receptor. (e) Two reported methods of generating spikes in individual artificial receptors include integrating an analog-to-digital converter (ADC) at each pixel and using sensing materials that can self-generate potential spikes. Circuit and photograph of one sensing pixel incorporating ADC adapted with permission from ref . Copyright 2019 American Association for the Advancement of Science. Schematics of self-spiking ion-electron conductor adapted with permission from ref . Copyright 2022 American Association for the Advancement of Science.
Figure 9.
Figure 9.
Subthreshold Schottky-barrier thin-film transistors (SB-TFTs). (a) Schematic of a metal oxide-based inorganic SB-TFT. IGZO, indium-gallium-zinc-oxide; LC, less compensated; MC, more compensated. (b,c) Measured input characteristics in linear scale (b), indicating VT (the threshold voltage), and in logarithmic scale (c), indicating Vref (the reference voltage), respectively. IG, gate leakage current. (d) Conceptual color bar of output power consumption (Pout) for a 1 V supply, normalized with W (channel width), clearly indicating each operational regime. Frames a–d adapted with permission from ref . Copyright 2016 American Association for the Advancement of Science. (e) Schematic of an organic SB-TFT. CYTOP, a commercial fluoropolymer; C8-BTBT, 2,7-dioctyl[1] benzothieno[3,2-b][1]benzothiophene; PS, polystyrene; PVC, polyvinyl cinnamate; PEN, polyethylene naphthalate. (f,g) Comparison of intrinsic gain (f) and transconductance efficiency (g) of different transistors. Organic SB-TFTs have the best performance. MOSFET, metal-oxide-semiconductor field-effect transistor. Frames e–g adapted with permission from ref . Copyright 2019 American Association for the Advancement of Science.
Figure 10.
Figure 10.
Challenges in achieving compatible sensor-biology interfaces. Arrows indicate the influence of one property (open dots) on another (solid dots). The issues stemming from different features of biological tissues are stated at the sensor-tissue interface and indicated by thick gray arrows.
Figure 11.
Figure 11.
Major innovations in materials and form factors, respectively, towards seamless sensor-biology interfaces. Materials innovations images (from left to right, top to bottom): Adapted with permission from ref . Copyright 2018 Springer Nature. Adapted under the terms of the Creative Commons CC BY license from ref ; published 2022 Springer Nature. Adapted under the terms of the Creative Commons CC BY license from ref ; published 2021 Springer Nature. Adapted with permission from ref . Copyright 2018 Springer Nature. Adapted with permission from ref . Copyright 2016 Springer Nature. Form factor images (from left to right, top to bottom): Adapted with permission from ref . Copyright 2013 Springer Nature. Adapted with permission from ref . Copyright 2015 Springer Nature. Adapted with permission from ref . Copyright 2017 American Chemical Society. Adapted with permission from ref . Copyright 2016 Springer Nature. Adapted with permission from ref . Copyright 2021 Springer Nature. Adapted with permission from ref . Copyright 2018 Springer Nature. PAA, polyacrylic acid; SBS, poly(styrene-butadiene-styrene).
Figure 12.
Figure 12.
An energy-efficient sensing system incorporating multiple strategies for power management: reliable and high-power ambient energy harvesting (representative devices for light, mechanical, and chemical energy harvesting are shown; full list of devices is provided in Table 4), large-capacity energy storage in flexible form factors, reliable and efficient wireless power transfer, and systemic power management. TENG, triboelectric nanogenerator; PTE, power transfer efficiency.
Figure 13.
Figure 13.
Outlook for flexible sensor networks. Black arrows denote connectivity. A group of sensor nodes can communicate within themselves and may be accessed by more than one cloud server. Due to dense connection, the two networks merge into one. Ultimately, numerous sensor nodes may be connected in a ‘meganetwork’ involving more gateways and servers (dot-outlined shapes). Cloud computing and fog computing will be instrumental in the realization of extensive sensor connectivity. Images for “imperceptible form factors”: Adapted with permission from ref . Copyright 2019 Springer Nature. Adapted with permission from ref . Copyright 2021 Springer Nature. Adapted with permission from ref . Copyright 2021 Springer Nature.
Figure 14.
Figure 14.
Key issues to address during the translation of flexible sensors from labs to end-users. Images under “Customers and competitors” designed by Freepik. Images under “Design” adapted with permission from ref . Copyright 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. Adapted with permission from ref . Copyright 2022 Wiley-VCH GmbH. Images under “Fabrication” adapted with permission from ref . Copyright 2020 Springer Nature. Adapted with permission from ref . Copyright 2021 American Association for the Advancement of Science. Images under “Modular system” adapted with permission from ref . Copyright 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. Adapted with permission from ref . Copyright 2016 Springer Nature. Images under “Printing” adapted with permission from ref . Copyright 2018 American Chemical Society. Adapted with permission from ref . Copyright 2021 American Chemical Society. Images under “Polymers & nanomaterials” adapted with permission from ref . Copyright 2021 American Association for the Advancement of Science. Adapted with permission from ref . Copyright 2021 Springer Nature. Images under “System integration” adapted with permission from ref . Copyright 2022 American Association for the Advancement of Science. Adapted with permission from ref . Copyright 2018 Springer Nature. Images under “Data security & privacy” and “Quality, safety & sustainability” designed by Freepik.
Figure 15.
Figure 15.
A vision for intelligent sensing systems with advantages listed on the left. Edge computing (including near-sensor and in-sensor computing) and neuromorphic computing are plausible ways of achieving sensor intelligence. They are not mutually exclusive and may be implemented in a single system. AI algorithms will be implemented in both central and edge processors. A feedback mechanism not only informs the user but also completes closed-loop operation. Integration with biological tissues permits two-way communication in various forms (details in Figure 16). Dashed black arrows indicate the direction of data flow. Images of non-neural network ML (machine learning) adapted with permission from ref . Copyright 2018 American Chemical Society. Neural network schematic adapted with permission from ref . Copyright 2020 Springer Nature. Deep learning schematic reproduced with permission from ref . Copyright 2015 Nature Publishing Group. Haptics image adapted with permission from ref . Copyright 2022 Springer Nature. Display image adapted with permission from ref . Copyright 2021 Springer Nature.
Figure 16.
Figure 16.
Evolution of flexible artificial sensory devices and systems for bio-integration. Memristor schematic adapted with permission from ref . Copyright 2010 American Chemical Society. Transistor schematic adapted with permission from ref . Copyright 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Electronic neural interface schematics adapted with permission from ref . Copyright 2020 Springer Nature. Pressure sensory artificial neuron and bio-integration images adapted with permission from ref . Copyright 2018 American Association for the Advancement of Science. Chemical sensory artificial neuron and bio-integration schematics adapted with permission from ref . Copyright 2022 Springer Nature.
Figure 17.
Figure 17.
A technology roadmap for flexible sensors and systems. Evolution with time is not drawn to scale. Images from left to right: Adapted with permission from ref . Copyright 2021 American Chemical Society. Adapted with permission from ref . Copyright 2021 Wiley-VCH GmbH. Adapted with permission from ref . Copyright 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Adapted with permission from ref . Copyright 2020 Springer Nature. Adapted with permission from ref . Copyright 2016 Nature Publishing Group.
Scheme 1.
Scheme 1.. A Holistic Approach to Sensor Accuracy Assurance during the Design, Manufacture, Validation, and Deployment of a Sensor Technologya
aFactors to consider are highlighted in red boxes.

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