Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
1998-11-02
2001-01-09
Stamber, Eric W. (Department: 2764)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C700S121000, C700S031000, C702S181000
Reexamination Certificate
active
06173240
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to analysis of simultaneous variation of one or more random variables, representing device structure or manufacturing process characteristics parameters), and to applications of the results to statistical analysis of semiconductor device behavior and semiconductor fabrication processes.
BACKGROUND OF THE INVENTION
Since the early 1960s, technology simulation has played a crucial role in the evolution of semiconductor integrated circuit (IC) technology. The initial simulation of fundamental operating principles of key structures, for example, bipolar and MOS transistors, has provided critical insight into the phenomena associate with the scaling of devices and has led to the development of large scale integration (LSI).
At present, technology simulation is best viewed within the framework of an overall IC Electronic Computer-Aided Design (ECAD) system. Starting from the basic fabrication input,—the individual process “recipes” and data to generate lithographic masks and layouts—these systems attempt to model a complete circuit from the physical structure of its components through to the estimation of overall system performance. As part of the overall ECAD framework, a Technology Computer-Aided Design (TCAD) system's main components are typically a process simulator and/or a device simulator, with appropriate interfaces that mimic experimental setups as closely as possible.
The device simulator's task is to analyze the electrical behavior of the most basic of IC building blocks, the individual transistors or even small sub-circuits, at a very fundamental level. As such, the simulator uses as inputs the individual structural definitions (geometry, material definitions, impurity profiles, contact placement) generated by process simulation, together with appropriate operating conditions, such as ambient temperature, terminal currents and voltages, possibly as functions of time or frequency. A TCAD system's principal outputs are terminal currents and voltages, appropriate for generating more compact models to be used in circuit simulations. A circuit simulator typically defines the interface to higher-level, application-specific synthesis tools. In principle, given a recipe and a set of masks, the TCAD tools themselves can build virtual device structures and then predict performance of entire circuits to a very high degree of accuracy. Modern process and device simulators are capable of rapid characterization, on the order of many structures per hour, as compared to experiments where on is sometimes fortunate to obtain a new structure within a period of a couple months.
At present, TCAD is extensively used during all stages of semiconductor devices and systems manufacturing, from the design phase to process control, when the technology has matured into mass production. Examples of this are discussed by R. W. Dutton and M. R. Pinto, “The use of computer aids in IC technology evolution”, Proc. I.E.E.E., vol. 74, no. 12 (December 1986) pp. 1730-1740, and by H. Hosack, “Process modeling and process control needs for process synthesis for ULSI devices”, (in) ULSI Science and Technology (1995). As a result of its application, TCAD offers enormous acceleration of the prototyping process for devices, integrated circuits and systems, because expensive manufacturing and metrology can be reduced to a minimum, or even become superfluous. Existing technologies, which are accurately represented in a TCAD environment, can be extrapolated into regions of more aggressively scaled down design rules.
In many semiconductor device fabrication processes, interest centers on variation of several variables or parameters that are random, not deterministic. These fabrication processes may include substrate formation, ion implantation, thermal diffusion, etching of a trench or via, surface passivation, packaging and other similar processes. Examples of random variables or parameters associated with these processes include concentration of a semiconductor dopant deposited in a semiconductor substrate, temperature of a substrate, temperature used for a thermal drive process for diffusion of a selected dopant into a substrate, initial impurity concentration of the substrate used for the thermal drive process, thickness of a substrate used for the thermal drive process, concentration of a selected ion used for implantation of the ion into a substrate, average ion energy used for implantation of an ion into a substrate, areal concentration of an ion used for implantation of the ion into a substrate, thickness of a substrate used for an ion implantation process, initial impurity concentration of a substrate used for an ion implantation process, desired incidence angle for etch into a substrate, desired etch distance into a substrate, and thickness of a passivation layer.
Two types of uncertainty affect one's confidence in modeling response of an electronic process, or of an electronic device produced by such a process: structural uncertainty, arising from use of inaccurate or incomplete models; and parametric uncertainty, arising from incomplete knowledge of the relevant variables and of the random distributions of these variables. Parametric uncertainty can be reduced by (1) refining measurements of the input variables that significantly affect the outcome of an electronic fabrication process and/or resulting device and (2) refining the knowledge of interactions between variations of two or more of the input variables and the effects of these joint variations on the process and/or resulting device.
Sensitivity of a fabrication process or a resulting device to variation of one or more of these variables may depend strongly upon the particular values of the other variables. One well known method of examining the effects of variation of one or more variables on a particular result is the Monte Carlo method, wherein each of the variables is varied, often independently, and a plurality of calculations is ran, one or more with each different permutation of the set of variable values. If interest centers on variation of N variables (N≧1), numbered n=1, 2, . . . , N, and if variable number n is to be examined for, say, K
n
discrete values, a conventional Monte Carlo approach will require running and analyzing K=K
1
×K
2
× . . . ×K
N
situations in order to fully explore the variable ranges and interactions of values of the variables. Each Monte Carlo sample or run to model one or more steps in a fabrication process may require use of a complex physical model in order to reflect the interactions of the variables, and each run may require many tens of seconds or minutes to complete. Further, the totality of runs may not adequately illuminate the joint effect of simultaneous variation of several variables because of the plethora of data generated.
What is needed is an approach that allows modeling of simultaneous variation of N random variables, where N is at least 1, so that subsequent Monte Carlo sampling can be performed in less time. Preferably, this approach should allow estimation of an N-variable model function and calculation of joint moments, such as means and variances, for any subset of the N variables. Preferably, this approach should allow presentation of at least some of the results in analytic form as well as in numerical form.
SUMMARY OF THE INVENTION
These needs are met by the invention, which provides an approach for determining a model function for an N-variable process (N≧1) related to modeling of output responses for semiconductor fabrication and semiconductor device behavior. An output response for a process or an electronic device is approximated by a model function G(x
1
, . . . ,x
N
) that is mathematically tractable and that can be used to estimate a probability density function that applies to the N variables jointly. Once obtained, the model function can be used to calculate statistical moments (mean, variance, skew, kurtosis, etc.) for these variables jointly or individually and can be used t
R{umlaut over (u)}hl Roland
Sepulveda Marcos
Choi Kyle J.
ISE Integrated Systems Engineering AG
Schipper John F.
Stamber Eric W.
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