Eyal Dassau

Modeling and temperature control of rapid thermal processing

In the past few years, rapid thermal processing (RTP) has gained acceptance as mainstream technology for semiconductor manufacturing. This single wafer approach allows for faster wafer processing and better control of process parameters on the wafer. However, as feature sizes become smaller, and wafer uniformity demands become more stringent, there is an increased demand from rapid thermal (RT) equipment manufacturers to improve control, uniformity and repeatability of processes on wafers. In RT processes, the main control problem is that of temperature regulation, which is complicated due to the high non-linearity of the heating process, process parameters that often change significantly during and between the processing of each wafer, and difficulties in measuring temperature and edge effects. This paper summarizes work carried out in cooperation with Steag CVD Systems, in which algorithms for steady state and dynamic temperature uniformity were developed. The steady-state algorithm involves the reverse engineering of the required power distribution, given a history of past distributions and the resulting temperature profile. The algorithm for dynamic temperature uniformity involves the development of a first-principles model of the RTP chamber and wafer, its calibration using experimental data, and the use of the model to develop a controller.

New product design via analysis of historical databases

Abstract A methodology is presented to define a set of operating conditions to produce a desired product, given a database of historical operating conditions and the product quality that they produced. This approach relies on the generation of a reliable model that can be used to predict the quality variables (the Y block) from the decision variables (the X block). Genetic programming (GP) is used to automatically generate accurate nonlinear models relating latent vectors for the X and Y blocks. The GP has the capability to carry out simultaneous optimization of model relationship structures and parameters, as well as to identify the most important basis functions. Once an adequate model is generated, it is used to predict the required process conditions to meet the new quality target by reverse mapping.

Optimization-based root cause analysis

Abstract A systematic approach to yield enhancement was recently proposed by Dassau et al [1], which combines six-sigma with design and control to improve estimated yields in the process design stage. After identifying the critical-to-quality variables, the key step involves the analysis of process measurements to identify the root cause for low quality or yield. The availability of a model of the process permits a significant improvement to this step, through the incorporation of formal optimization as a means of automating root cause analysis. The problem is formulated as a mixed-integer nonlinear program, whose system variables include the possible perturbations that affect low quality and low yield, and whose decision variables are all of the possible process improvements. The potential of proposed optimization-based root cause analysis to enhancing yield is demonstrated on the process of penicillin production, involving both fermentation and downstream purification.

Effective process design instruction: From simulation to plant design

Abstract This paper describes a new, three-semester sequence of courses, in which students are taught the theory and practice of developing a complete process package. The sequence first teaches the efficient usage of process simulation, followed by a formal process design course, and then rounds off by imparting the skills necessary to perform detailed equipment design. A unique feature is the usage of the same design project, at increasing levels of detail in each of the courses in the sequence.