A Fast and Low-Power Detection System for the Missing Pin Chip Based on YOLOv4-Tiny Algorithm

In the current chip quality detection industry, detecting missing pins in chips is a critical task, but current methods often rely on inefficient manual screening or machine vision algorithms deployed in power-hungry computers that can only identify one chip at a time. To address this issue, we prop...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.3918
Main Authors: Chen, Shiyi, Lai, Wugang, Ye, Junjie, Ma, Yingjie
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
chip detection
Computer terminals
Computer vision
Deep learning
Defects
Detectors
Digital integrated circuits
Efficiency
Feature maps
Field programmable gate arrays
FPGA
Image detection
inference accelerator
Libraries
low-power system
Machine vision
Motion picture theaters
Neural networks
Power
Tiling
Vision systems
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Summary:In the current chip quality detection industry, detecting missing pins in chips is a critical task, but current methods often rely on inefficient manual screening or machine vision algorithms deployed in power-hungry computers that can only identify one chip at a time. To address this issue, we propose a fast and low-power multi-object detection system based on the YOLOv4-tiny algorithm and a small-size AXU2CGB platform that utilizes a low-power FPGA for hardware acceleration. By adopting loop tiling to cache feature map blocks, designing an FPGA accelerator structure with two-layer ping-pong optimization as well as multiplex parallel convolution kernels, enhancing the dataset, and optimizing network parameters, we achieve a 0.468 s per-image detection speed, 3.52 W power consumption, 89.33% mean average precision (mAP), and 100% missing pin recognition rate regardless of the number of missing pins. Our system reduces detection time by 73.27% and power consumption by 23.08% compared to a CPU, while delivering a more balanced boost in performance compared to other solutions.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23083918