Integration and test strategies for semiconductor manufacturing equipment

The complexity of semiconductor manufacturing equipment is growing. This growth results in a complexity increase of the integration and test phase of these systems. Simply adding more test resources is not possible anymore, because of the cost involved. A better design of an integration and test strategy can help to optimize this hectic phase. However, methods to design and evaluate integration and test strategies for multi-disciplinary systems are hardly available. In this paper, we present a method to design and compare integration and test strategies. Following this method, an optimal integration and test strategy can be chosen from a set of possible strategies. A case has been performed where a system is integrated and tested using three different integration and test strategies: a time-to-market-driven strategy, a qualitydriven strategy and a combined quality and time-to-market strategy. Introduction The semiconductor manufacturing business has evolved from a niche to one of the main technology enablers in the last fifty years. The well known Moore’s Law (Moore 1965) predicted that the number of transistors in an integrated circuit (IC) would double every 18 months. This has indeed happened and it resulted in the ability to use ICs in more complex systems for more complex tasks. The speed of innovation in this business is driven mainly by the capabilities of newly developed IC manufacturing equipment. Each new generation of manufacturing equipment leads to the ability to manufacture thinner IC-lines faster. Each new generation also leads to an increase of complexity, because of the tighter specifications. The number of components, interfaces and multi-disciplinary nature of these components result in an integration and test phase which is difficult to predict and control. This is the main reason for us to investigate the impact of integration and test strategies for an wafer scanner, developed and manufactured by ASML (ASML, 2005). Nowadays, integration and test strategies are defined manually based on expert knowledge. Defining an integration and test strategy can be an easy task for small mono-disciplinary systems. Experts can manually adapt integration and test strategies such that the optimal parameters like time-to-market and cost are taken into account. Comparing strategies is done using known mono-disciplinary criteria, like the amount of code coverage for software systems and the number of tested inputs and outputs for electronic boards. 1 This work has been carried out as part of the TANGRAM project (www.esi.nl/tangram) and partially supported by the Netherlands Ministry of Economic Affairs under grant TSIT2026. Mono-disciplinary methods often cannot be used to integrate and test large multi-disciplinary systems. Therefore, integration and test strategies are still developed by experts. The main reason for the design of integration and test strategies by experts is that multi-disciplinary methods to design and evaluate these strategies hardly exist. In this paper, we present a method to describe, analyze and evaluate multi-disciplinary integration and test strategies. The structure of this paper is as follows. In the section “Integration and test strategies” a definition of an integration and test strategy is given and the key performance indicators of such a strategy are defined. The next section “Modeling and analysis of integration and test strategies” describes a method to model and compare integration and test strategies. This method is applied in the following section on two cases: a time-to-market-driven strategy and a qualitydriven strategy. This paper is finalized with conclusions and references. Integration and test strategies An integration and test strategy consists of the following elements: • an integration and test sequence, consisting of one or more integration phases and one or more test phases, • a test process configuration and • the test stop criteria for each test phase. Integration and test strategies should be compared using criteria applicable to a variety of systems. These high level criteria (key performance indicators) that characterize integration and test strategies are: Φ Total integration and test duration, defined as the time from the start of the integration and test phase until the moment of meeting a stop criterion for stopping this phase. C Integration and test cost, defined as the sum of the costs of all assembly actions, disassembly actions, actions to execute test cases, diagnosis actions, actions on developing fixes for observed faults and applications of these fixes during the test phase. R R Remaining risk, which is our measure for quality and is defined as the risk which remains in the system after the stop criterion is reached for the integration and test phase. The risk in the system can be determined by summing the risk which is in the system for each possible fault s . The risk for each possible fault s can be calculated by multiplying the probability that the fault exists in the system with the impact of that fault if it exists in the system: ) ( ) ( ) ( s I s P s R = . The remaining risk can be determined at the end of an integration and test phase using the remaining possible faults in the system and the impact of these fault states. Consequently, the risk at any point in time can be calculated also. These key performance indicators are illustrated by an example telephone system consisting of three modules: a handset, a cable, a device and the interfaces between handset and cable and cable and device. Figure 1 is a graphical overview of the telephone system. Interface between device and cable Interface between horn and cable ho rn device