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This paper aims to introduce a compact and low-cost robotized system and corresponding processing method for automatically identifying and de-stacking circulation boxes under natural stacking status.

The whole system is composed of an industrial robot, a laser scanner and a computer. Automated de-stacking requires comprehensive and accurate status information of each box. To achieve this goal, the robot carries the laser scanner to perform linear scanning to describe a full depth image for the whole working area. Gaussian filter is applied to the image histogram to suppress the undesired noise. Draining and flooding process derived from classic algorithm identifies each box region from an intensity image. After parameters calculation and calibration, the grasping strategy is estimated and transferred to the robot to finish the de-stacking task.

Currently, without pre-defined stack status, there is still manual operated alignment in stacking process in order to enable automatic de-stacking using robot. Complicated multi-sensor system such as video cameras can recognize the stack status but also brings high-cost and poor adaptability. It is meaningful to research on the efficient and low-cost measurement system as well as corresponding common data processing method.

This research presents an efficient solution to automated de-stacking task and only tests for three columns stack depending on the actual working condition. It still needs to be developed and tested for more situations.

Utilizing only single laser scanner to measure box status instead of multi-sensor is novel and identification method in research can be suitable for different box types and sizes.

Semiconductor manufacturing industry requires highly accurate robot operation with short install/setup downtime.

We develop a fast, low cost and easy‐to‐operate calibration system for wafer‐handling robots. The system is defined by a fixture and a simple compensation algorithm. Given robot repeatability, end effector uncertainties, and the tolerance requirements of wafer placement points, we derive fixture design and placement specifications based on a statistical tolerance model.

By employing the fixture‐based calibration, we successfully relax the tolerance requirement of the end effector by 20 times.

Semiconductor manufacturing requires fast and easy‐to‐operate calibration systems for wafer‐handling robots. In this paper, we describe a new methodology to solve this problem using fixtures. We develop fixture design criteria and a simple compensate algorithm to satisfy calibration requirements. We also verify our approach by a physical example.

The potential benefits that a firm can expect from an automated factory, the economic justification and the steps for implementing flexible manufacturing systems are discussed. Some British companies are used as case examples. They all point to the need for the UK to implement the automated factory. For success in this, attention must be given to management practices, support systems, performance measurement, cost management and data collection.

The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to market while overseeing a company. The paper aims to discuss these issues.

The interviewee is Dr Rob Buckingham, Director at UK Atomic Energy Authority (UKAEA) and Robotics Pioneer. Dr Buckingham is an innovator of snake-arm robotics for confined and hazardous environments. In this interview, Dr Buckingham shares some of his 30+ year personal and business experiences of working in industry, academia, co-founding and directing a robotics company and heading up a new UK government-funded organization for remote handling.

Dr Buckingham received his BSc and his MEng in the Special Engineering Programme at Brunel University in London. The program’s objective was to train engineers for the industry by developing problem-solving abilities and decision-making skills of students, which Buckingham accomplished while being sponsored by the UKAEA and as a National Engineering Scholar. After obtaining his PhD in robotics at the University of Bristol, Buckingham, he remained at Bristol for two years as a lecturer in mechanical engineering. In 1997, he co-founded OC Robotics, a private company that designs snake-arm robots specifically to operate in confined spaces. Buckingham directed OC until 2014, when he returned to where he began his early career, UKAEA Culham, this time as a Director and Head of the new Remote Applications in Challenging Environments (RACE) Centre. Under Buckingham’s leadership, RACE is involved in exploring many areas of remote operations, including inspection, maintenance and decommissioning and will be instrumental in developing new remote tools and techniques for academia and industry.

With the unique experience of studying at a university’s distinctive engineering program while working as a young engineer for the UKAEA who sponsored him, Dr Buckingham found his lifelong passion and career in robotics for remote handling. He was one of the creators of the emerging field of snake-arm robotics. He is now applying his innovative, commercial technologies and strategies from working in the nuclear, aerospace, construction and petrochemicals sectors to the industry of nuclear fusion. Dr Buckingham was awarded The Royal Academy of Engineering Silver Medal in 2009. In the same year, his company OC Robotics won the Queen’s Award for Enterprise in the category of Innovation. Buckingham is also a Fellow of the UK Institute of Engineering Technology, a Fellow of the Royal Academy of Engineering and a visiting professor at the Bristol Robotics Laboratory. He was co-chair of the Robotics and Autonomous Systems (RAS) Special Interest Group Steering Group during the preparation of the influential UK RAS strategy, which has since been adopted by UK Government.

The electronics industries are relying increasingly on robotics for their production. Wafer handling robots are used to transfer wafers between wafer processing stations. A pick‐measure‐place method is typically utilized to transfer wafers accurately. The measurement step is performed using an aligner, which is time‐consuming. To increase wafer transfer efficiency, it is desirable to speed up the measurement process or place it in parallel with other operations. To solve the problem, optic sensors are installed at each station to estimate the wafer eccentricity on‐the‐fly. The eccentricity values are then applied to control the robot to place the wafer directly onto another station accurately without using the aligner. However, current methods face problems to achieve high accuracy requirements to meet the electronic manufacturing needs. The purpose of this paper is to develop a technique to improve the wafer handling performance in semiconductor manufacturing.

The kinematics model of the wafer handling robot is developed. Two sensor location calibration algorithms are proposed. Method I is based on the wafer handling path. Method II uses the offset paths from the wafer handling path. The results from these two methods are compared. To compute the wafer eccentricity on‐the‐fly, a wafer eccentricity estimation technique is developed.

The developed methods are implemented using a wafer handling robotic system in semiconductor manufacturing. The wafer eccentricity estimation errors are greatly reduced using the developed methods. The experimental results demonstrate that Method II achieves better results and can be used to improve the wafer handling accuracy and efficiency.

The proposed technique is implemented and tested many times on a wafer handing robotic system. The notch alignment in the wafer handling needs further research.

The developed method is validated using a system in semiconductor manufacturing. Hence the developed method can be directly implemented in production if the notch of a wafer can be identified.

This paper provides techniques to improve the wafer handling accuracy in semiconductor manufacturing. Compared with the results using other methods, Method II greatly increases the wafer handling accuracy to satisfy the semiconductor manufacturing needs.