Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI

Abstract

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.

Machine-learning based characteristic estimation method in printed circuit board production lines

“…On active responses, the simulated results followed the expectation because the coupling efficiency increased as the capacitance values increased [15], which led to the decay of transmitted intensity S 21 (figure 6(a)) in the detection setup (figure 7). However, the measured results showed a reversed tendency (figure 6(b)).…”

“…Based on the referential literature [15], the fundamental Archimedean spiral was described by (1) in the Cartesian coordinate, in which a, b, and t were constant, constant, and sector angle, respectively. To meet the EM operation requirements (frequency of 2-6 GHz that supports Wi-Fi and Bluetooth applications), a, b, and t were defined as 0.591, 1, and 4.28π-12.86π, respectively; and the resulting pattern was 15 cm-long with line width (w) and line space (s) both 0.93 mm.…”

“…Regular ink accumulations could be found not only because of the pattern design [17] but also the printing sequence of the system [18]. As a result, repeated bulges and neckings appear on the patterns and literature described this effect by duplicating them through sinusoidal equations [15] with (2). The constant 25 in (2) indicated that there were 25 bulges along one arm.…”

“…Although literature has indicated potential relationships between pattern distortion and its EM performance [9][10][11][12][13], they failed to provide solutions to predict the results. Researchers also tried to establish related system and procedures to predict some responses of Archimedean spiral antennas through their fabrication features, results were incomplete due to impractical target design, non-optimized software and hardware modules, insufficient data, low confidence level of results, and non-integrated humanmachine interface (table 1) [14,15].…”

“…To overcome the disadvantages of existing systems, this work adopted identical analysis procedure from our previous work [15], however improved the target antenna distortions with equal bulge amplitudes (BAs) on both edges of both arms with additional signal for analysis, and revised machine learning (ML) and artificial intelligence (AI) models. As a result, the novelty and contribution of this paper are summarized as follows: (1) integrated hardware and software through a human-machine interface and (2) improved AOI and AI for accurate predicted EM characteristics.…”