US 11,910,083 B2
Method and system with optimization of lens module assembly
Hye Geun Min, Suwon-si (KR); Ye Rim Choi, Seoul (KR); Yeon Bin Son, Siheung-si (KR); Sung Hoon Kim, Gwacheon-si (KR); and Eun Young Choi, Anyang-si (KR)
Assigned to Samsung Electro-Mechanics Co., Ltd., Suwon-si (KR); and KYONGGI UNIVERSITY INDUSTRY & ACADEMIA COOPERATION FOUNDATION, Suwon-si (KR)
Filed by SAMSUNG ELECTRO-MECHANICS CO., LTD., Suwon-si (KR); and KYONGGI UNIVERSITY INDUSTRY & ACADEMIA COOPERATION FOUNDATION, Suwon-si (KR)
Filed on Aug. 9, 2021, as Appl. No. 17/397,136.
Claims priority of application No. 10-2020-0147445 (KR), filed on Nov. 6, 2020; and application No. 10-2021-0023177 (KR), filed on Feb. 22, 2021.
Prior Publication US 2022/0150404 A1, May 12, 2022
Int. Cl. H04N 5/335 (2011.01); H04N 23/60 (2023.01); G06N 3/126 (2023.01); H04N 23/617 (2023.01)
CPC H04N 23/64 (2023.01) [G06N 3/126 (2013.01); H04N 23/617 (2023.01)] 23 Claims
OG exemplary drawing
 
1. A method with optimization of a lens module assembly, comprising:
in preparing a lens module including assembled N lenses respectively formed in cavities to overlap each other on an optical axis, wherein N is a natural number of 2 or more:
receiving, by a computing system, characteristic information of at least N lenses respectively formed in N cavity groups each including a respective plurality of cavities having a similarity to each other that is higher than a similarity to each other of the assembled N lenses; and
processing, by the computing system, information for selecting N cavities from the N cavity groups, based on the characteristic information,
wherein the computing system receives or stores a past cavity selection result obtained by the processing of the information for selecting the N cavities, a fitness function configured based on data of the assembled N lenses or data of the prepared lens module according to the past cavity selection result, and a genetic algorithm, and
wherein the processing of the information for selecting the N cavities includes updating chromosome entity information based on the fitness function and output chromosome information crossed or mutated based on the genetic algorithm from input chromosome information corresponding to the past cavity selection result, and processing the information for selecting the N cavities based on the chromosome entity information and the characteristic information.