This book is about the design and application of industrial cleanroom robots in electronics manufacturing. It is intended as a hands-on technical reference for engineers and factory managers involved in manufacturing electronic devices in cleanroom environments. The book provides insight into the principles and applications of industrial cleanroom robotics, in particular in semiconductor manufacturing, the most demanding process in terms of cleanliness requirements. Other examples are the hard disk, flat panel display, and solar industries, which also use high levels of cleanroom automation and robotics. In contrast to the complex manufacturing process, the typical robotic designs often utilize relatively simple robot kinematics in the highly structured environments of process and metrology tools. Some industries, for example the semiconductor front-end industry, are governed by technical standards and guidelines, which are generally helpful during the design process of robotic systems. On the other hand, robotic engineers in electronics manufacturing face challenges that are unknown in other markets, most importantly the cleanliness required in certain factories. Strict cleanliness requirements have resulted in two categories of cleanroom robots: ‘atmospheric robots’ for high-quality cleanliness at ambient atmospheric pressure, and ‘vacuum robots’ for extreme cleanliness in enclosures under various vacuum pressures. These two categories are the focus of this book.

Robotics refers to the study and use of robots (Nof, 1999). Likewise, industrial robotics refers to the study and use of robots for manufacturing where industrial robots are essential components in an automated manufacturing environment. Similarly, industrial robotics for electronics manufacturing, in particular semiconductor, hard disk, flat panel display (FPD), and solar manufacturing refers to robot technology used for automating typical cleanroom applications. This chapter reviews the evolution of industrial robots and some common robot types, and builds a foundation for Chapter 2, which introduces cleanroom robotics as an engineering discipline within the broader context of industrial robotics.

History of industrial robotics

Visions and inventions of robots can be traced back to ancient Greece. In about 322 BC the philosopher Aristotle wrote: “If every tool, when ordered, or even of its own accord, could do the work that befits it, then there would be no need either of apprentices for the master workers or of slaves for the lords.” Aristotle seems to hint at the comfort such ‘tools’ could provide to humans. In 1495 Leonardo da Vinci designed a mechanical device that resembled an armored knight, whose internal mechanisms were designed to move the device as if controlled by a real person hidden inside the structure. In medieval times machines like Leonardo's were built for the amusement of affluent audiences. The term ‘robot’ was introduced centuries later by the Czech writer Karel Capek in his play R. U. R. (Rossum's Universal Robots), premiered in Prague in 1921.

Manufacturing in cleanroom environments

Clean environments are required for manufacturing modern electronics devices, in particular semiconductor devices, but also hard disks, flat panel displays (FPDs), and solar panels. Wafer processing in the semiconductor industry includes some of the most demanding processes in terms of complexity and cleanliness, due to the submicron dimensions of modern semiconductor devices. This book focuses on industrial cleanroom robotics in semiconductor and FPD manufacturing. Both industries experienced phenomenal technical advancement and growth in the 1980s and 1990s and have established manufacturing facilities in several geographic regions: North America, Europe, and Asia/Pacific Rim. India may emerge as another manufacturing region. The market for semiconductor manufacturing equipment was valued at approximately US$45.5 billion in 2007. The market for FPD manufacturing equipment surpassed the US$1 billion mark in 1997 for the first time. In 2008 it was estimated at US$10 billion.

Cleanroom requirements

Cleanrooms are isolated environments in which humidity, temperature, and particulate contamination are monitored and controlled within specified parameters (SEMI standard E70). Particulates are fine particles, solid or liquid, that are suspended in a gas. Particulate sizes range from less than 10 nm to more than 100 µm. Particulates of less than 100 nm are called ultra-fine particles. Here the term ‘particle’ is used throughout, representing particles of all applicable sizes, either suspended in a gas or attached to a surface. Cleanroom environments are required if particle contamination is a concern, as is the case, for example, in semiconductor manufacturing.

This chapter presents procedures and relevant standards for testing and characterizing robots designed for electronics manufacturing in cleanroom environments. Several robot performance parameters are of interest: airborne particle contamination, surface particle contamination, positioning accuracy and repeatability, path accuracy and repeatability, vibration, and axis decoupling. The presented examples apply directly to substrate handling in semiconductor manufacturing and related industries. In the 1970s a typical semiconductor product yield, the number of semiconductor devices suitable for sale compared to the total number manufactured, was 10–15% in the USA. In Japan 60–90% was achieved through rigorous contamination control (Donovan, 2001). Today (2009) the product yield in modern semiconductor factories exceeds 90%, the result of contamination control equipment and procedures. Sections 7.1 and 7.2 present particle contamination tests.

Airborne particle contamination

Airborne particle contamination (APC) generated by substrate-handling robots can be a critical performance index, depending on the cleanliness requirements, and must be tested and characterized under normal robot operating conditions. The number of airborne particles is measured at selected locations in the immediate vicinity of the robot. For example, Sematech standard 92051107A-STD recommends measuring ‘particles per wafer pass,’ a performance criterion specified in SEMI standard E14. Substrate-handling robots should be tested and certified as subassemblies, and their contamination contribution specified as part of the total tool contamination allowance. An APC test procedure for individual robots is described below. Note that APC is different from airborne molecular contamination (AMC), a non-particle gaseous substance that can also be detrimental to a product or process.

Nonmonotonic or Defeasible Reasoning

Those AI researchers called logicists, who favor the use of logical languages for representing knowledge and the use of logical methods for reasoning, acknowledge one problem with ordinary logic; namely, it is monotonic. By that they mean that the set of logical conclusions that can be drawn from a set of logical statements does not decrease as more statements are added to the set. If one could prove a statement from a given knowledge base, one could still prove that same statement (with the very same proof!) when more knowledge is added.

Yet, much human reasoning does not seem to work that way – a fact well noticed (and celebrated) by AI's critics. Often, we jump to a conclusion using the facts we happen to have, together with reasonable assumptions, and then have to retract that conclusion when we learn some new fact that contradicts the assumptions. That style of reasoning is called nonmonotonic or defeasible (meaning “capable of being made or declared null and void”) because new facts might require taking back something concluded before.

One can even find examples of nonmonotonic reasoning in children's stories. In That's Good! That's Bad!, by Margery Cuyler, a little boy floats high into the sky holding on to a balloon his parents bought him at the zoo. “Wow! Oh, that's good,” the story goes.

For a system to be intelligent, it must have knowledge about its world and the means to draw conclusions from, or at least act on, that knowledge. Humans and machines alike therefore must have ways to represent this needed knowledge in internal structures, whether encoded in protein or silicon. Cognitive scientists and AI researchers distinguish between two main ways in which knowledge is represented: procedural and declarative. In animals, the knowledge needed to perform a skilled action, such as hitting a tennis ball, is called procedural because it is encoded directly in the neural circuits that coordinate and control that specific action. Analogously, automatic landing systems in aircraft contain within their control programs procedural knowledge about flight paths, landing speeds, aircraft dynamics, and so on. In contrast, when we respond to a question, such as “How old are you?,” we answer with a declarative sentence, such as “I am twenty-four years old.” Any knowledge that is most naturally represented by a declarative sentence is called declarative.

In AI research (and in computer science generally), procedural knowledge is represented directly in the programs that use that knowledge, whereas declarative knowledge is represented in symbolic structures that are more-or-less separate from the many different programs that might use the information in those structures. Examples of declarative-knowledge symbol structures are those that encode logical statements (such as those McCarthy advocated for representing world knowledge) and those that encode semantic networks (such as those of Raphael or Quillian). Typically, procedural representations, specialized as they are to particular tasks, are more efficient (when performing those tasks), whereas declarative ones, which can be used by a variety of different programs, are more generally useful.