Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing
Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing
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DOI：
10.1002/aisy.202000202
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发表时间：
2021-02-01
影响因子：
7.4
通讯作者：
Lee, Kyusang
中科院分区：
文献类型：
--
作者：
Lee, Hee Sung;Baek, Yongmin;Lee, Kyusang
为了防止缺陷引起的潜在问题，缺陷识别一直是各个领域的一项重要任务。人们非常关注开发使用计算系统从图像中准确提取缺陷信息而没有人为错误的技术。然而，使用基于冯诺依曼结构的传统计算技术的图像分析正面临瓶颈，以有效地处理大量的输入数据，在低功耗和高速度。在这里，有效的缺陷识别证明通过形态学图像处理与最小的功耗，使用氧化物晶体管和基于忆阻器的交叉阵列，可以应用于神经形态计算。使用硬件和软件协同设计的神经形态系统结合动态高斯模糊内核操作，增强的缺陷检测性能被成功地证明了与传统的基于互补金属氧化物半导体（CMOS）的数字实现相比，具有约10（4）倍的功率效率计算。据信，后端线（BEOL）兼容的基于全氧化物的忆阻交叉杆阵列为通用人工智能（AIoT）应用提供了独特的潜力，其中传统硬件几乎无法使用。
Defect identification has been a significant task in various fields to prevent the potential problems caused by imperfection. There is great attention for developing technology to accurately extract defect information from the image using a computing system without human error. However, image analysis using conventional computing technology based on Von Neumann structure is facing bottlenecks to efficiently process the huge volume of input data at low power and high speed. Herein efficient defect identification is demonstrated via a morphological image process with minimal power consumption using an oxide transistor and a memristor-based crossbar array that can be applied to neuromorphic computing. Using a hardware and software codesigned neuromorphic system combined with a dynamic Gaussian blur kernel operation, an enhanced defect detection performance is successfully demonstrated with about 10(4) times more power-efficient computation compared to the conventional complementary metal-oxide semiconductor (CMOS)-based digital implementation. It is believed the back end of line (BEOL)-compatible all-oxide-based memristive crossbar array provides the unique potential toward universal artificial intelligence of things (AIoT) applications where conventional hardware can hardly be used.