Session: 16-02-01: Poster Session: NSF Research Experience for Undergraduates (REU)

Paper Number: 77119

Start Time: Wednesday, 02:25 PM

77119 - High-Speed Passive Autofocus Control of High Magnification Lenses Using Nanometer Precision Piezo Actuation 

Production quality control relies heavily on the inline inspection of manufacturing processes, e.g., the real-time monitoring and metrology for the Roll-to-Roll (R2R) printing of flexible electronics. This method of manufacturing holds promise for meter-scale width, high rate (m/sec) nano-, and micro-surface-patterning (1). Many high-speed and high-accuracy image acquisition, image processing, and pattern assessment methods facilitate the measurement of electronic pattern production. These methods range from visual analysis of condensed nitrogen vapor (2) to all-optical difference engines for digital defect detection (3). Despite their accuracy, many of these methods are insufficient for real-time inline industrial inspection mainly due to slower autofocus (AF) methods. Similarly, fast and continuous image-based AF is also critical for many other scenarios, such as the imaging and metrology of biological samples or scenarios where non-image-based AF methods pose risks to damaging heat- or photo-sensitive biological samples.

Traditionally, AF methods can be divided into two categories, active and passive. Active autofocus methods directly measure the distance between the camera and target and serve as an excellent precursor to many machine vision processes requiring highly focused images. Passive autofocus methods differ from active in that they attempt to maximize the contrast of the image by employing a searching algorithm. Both passive and active autofocus methods have their respective limitations; however passive systems that enable high-speed defect detection are significantly more attractive to the manufacturers as these methods can decrease the operating cost of production and do not pose any risk to damaging samples. For this reason, fast and accurate passive autofocus methods have been explored as a cheaper and safer alternative to active methods in a variety of scenarios (4).

We proposed a fast and accurate passive autofocus algorithm using Gaussian standard deviation and gradient-based binning (GB) (4).  The proposed algorithm directly calculates the mean of the Gaussian-shaped focus measure (FM) curve to find the optimal focus location and uses the FM curve standard deviation to adapt the motion step size. The calculation only requires 3-4 defocused images to identify the center location of the FM curve.  We experimentally observed that 77.4% of the overall GB method’s AF time is attributed to the AF step motor used for position control. For an AF time of 500ms, the motor movement time will contribute to 387ms. Therefore, upgraded hardware is necessary to evaluate an AF algorithm that is as fast as the proposed method. The stepper motor can be upgraded to a faster driver, e.g., piezo driver. The piezo driver only takes 3.3ms for a full-range movement. A driver this fast will theoretically reduce the motor motion time to less than 1% of the experimental AF step motor.

Meanwhile, imaging R2R printed patterns in high dynamic scenarios requires incredibly fast and precise position control of high-mass and high magnification lenses. Recently, piezoelectric actuators have been employed for mobile phone passive autofocus due to their superior speed, accuracy, and size compared to traditional stepper and servo motors (5). Moreover, high precision piezoelectric actuators have been integrated into active and passive autofocus systems for imaging of stationary microscope slides (6); however, neither active nor passive methods have been explored in dynamic scenarios. Developing, integrating, and evaluating a passive autofocus technology that enables real-time defect detection in dynamic scenarios is essential if R2R is to become the leading method for manufacturing flexible electronics globally.

1.          C. Merian, X. Du, D. Hardt, H. AlQahtani, (ASME Digital Collection), 2016.

2.          J. Yan, R. Ma, X. Du, Meas. Sci. Technol. 32, 105405 (2021).

3.          X. Feng, R. Su, T. Happonen, J. Liu, R. Leach, Opt. Express. 26, 13927–13937 (2018).

4.          P. DiMeo, L. Sun, X. Du, X. Du, Opt. Express. 29, 19862–19878 (2021).

5.          H.-P. Ko, H. Jeong, B. Koc, J. Electroceramics. 23, 530 (2008).

6.          C. Li et al., Micromachines. 11, 216 (2020).

 

Presenting Author: Peter DiMeo University of Massachusetts Amherst

Authors:

Peter DiMeo University of Massachusetts Amherst
Xian Du University of Massachusetts Amherst