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In this study, a method that is able to estimate the electromagnetic characteristic of spiral antennas was proposed and realized through consecutive procedures of automatic optical inspection (AOI), machine learning (ML), and artificial intelligence (AI), providing a solution to smart manufacturing. Two-arm self-complementary Archimedean spiral antennas (SCASAs) were introduced as examination targets with pattern distortions from potential process variations, in which bulges and neckings were mathematically generated to imitate uncontrollable ink rheology in printed and flexible electronics, covering the unexplored parts in previous works. The SCASAs in the training group were fabricated by standard printed circuit board procedures, and their pattern integrity in terms of line edge roughness (LER) and coupling frequency were collected through AOI for ML as the feature and label, respectively. The established AI model was based on Gaussian process regression with covariance function of exponential that showed the smallest root-mean-square-error and the largest coefficient of determination through iterative lazy-learning. By feeding the LERs of the SCASAs into the testing group, their corresponding coupling frequencies were estimated by the established AI model with high confidence level. Good linearity between the estimated and measured responses indicated that a reliable AI model and procedure were built, which outperforms existing methods that are unable to project off-line active characteristics of microelectronic components from their in-line pattern integrities.

In this report, Bovine Serum Albumin-based Metal–Insulator–Metal structures on Indium doped Tin Oxide coated flexible Polyethylene Terephthalate substrates with Aluminum (Al) as the top electrode have been fabricated and studied in detail. The thickness of the film was measured using a Field Emission Scanning Electron Microscope. The Fourier Transform Infrared Attenuated Total Reflectance spectrum confirms the presence of both amide-I and amide-II in the protein film. Ultraviolet-Visible Spectroscopy was performed for the absorption spectrum used to extract the optical bandgap. The surface roughness of the film was estimated by Atomic Force Microscopy. Electrical characterization of the devices gives a high capacitance density with a negative quadratic coefficient and a low dissipation factor, showing their potential for Radio Frequency/analog application. The number of dipoles (NPD) is found to be 1.01 × 1018 cm−3 with a permanent dipole moment (μ0) of 2.92 × 10−25 C m at an applied frequency of 1 MHz. The devices show high reliability resistant to degradation, studied by Constant Voltage Stressing. We also examined the performance of these flexible devices by repeated bending with different bending radii.

In this paper, five sensing mechanisms including capacitive, piezoresistive, inductive, piezoelectric, and optical are reviewed in details with representative literature. Basic functions of normal force, shear force, shear angle, torsional angle, rotational angle; and characteristics in sensitivity, dynamic window, and spatial resolution are discussed and categorized clearly in this article. In addition, materials used in the flexible force sensors for electrodes, substrates, and deformable dielectrics are highlighted and summarized. Furthermore, structural design, specifications of sensitivity, spatial resolution, dynamic range and cyclic life are also unambiguously outlined. Meanwhile, key design considerations are listed and evaluated in this review to indicate major contributions to the characteristics of the flexible force sensors. Notable challenges, potential business, and examples commercial players in the market are listed at the end of this manuscript. This article offers a stepping stone for those who plan to enter the flexible force sensor fields by revisiting the outcomes from the past decades.

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