The authors describe the MIT microelectromechanical computer-aided design system (MEMCAD), in which selected commercial software packages are linked with specialized database and numerical programs to allow designers to quickly perform both mechanical and electrical analyses of structures either described directly, or derived from the design specification (mask data plus process flow). The system architecture, the various modules, and their present status are described, and present system performance is demonstrated with several examples. >

A computer-aided design system for microelectromechanical systems (MEMCAD)

In this paper we review the current status of device simulation of ionizing-radiation-induced charge collection and single-event upset (SEU), with an emphasis on significant results of recent years. We present an overview of device-modeling techniques applicable to the SEU problem and the unique challenges this task presents to the device modeler. We examine unloaded simulations of radiation-induced charge collection in simple p/n diodes, SEU in dynamic random access memories (DRAM's), and SEU in static random access memories (SRAM's). We conclude with a few thoughts on future issues likely to confront the SEU device modeler.

Device simulation of charge collection and single-event upset

This paper reviews techniques for physics-based device-level simulation of single-event effects (SEEs) in Si microelectronic devices and integrated circuits. Issues for device modeling of SEE are discussed in the context of providing physical insight into mechanisms contributing to SEE as well as providing predictive capabilities for calculation of SEE rates. Recent advances in device simulation methodology are detailed, including full-cell simulations and cross-section calculations from first principles. Examples of the application of physics-based SEE simulations are presented, including scaling trends in soft error sensitivity as predicted by device simulation, single-event latchup (SEL) simulations in CMOS structures, and recent simulations of single-event transient (SET) production and propagation in digital logic circuits.

Physics-based simulation of single-event effects

Rapid modeling and optimization of manufacturing processes, devices, and circuits are required to support modern integrated circuit technology development and yield improvement. We have prototyped and applied an integrated system, called DOE/Opt, for performing Design of Experiments (DOE), Response Surface Modeling (RSM), and Optimization (Opt). The system to be modeled and optimized can be either physical or simulation based. Within the DOE/Opt system, coupling to external simulation or experimental tools is achieved via an embedded extension language based on Tcl. The external problem then appears to DOE/Opt as a model with user defined inputs and outputs. DOE/Opt is used to generate splits for experiments, to dynamically build and evaluate regression models from experimental runs, and to perform nonlinear constrained optimizations using either regression models or embedded executions. The intermediate regression modeling can appreciably accelerate the optimization task when simulation or physical experiments are expensive. The primary application of DOE/Opt has been to process optimization using coupled process and device simulation. DOE/Opt has also been applied to process and device simulator tuning, and to aid in device characterization. Such a DOE/Opt system is expected to augment the use of TCAD tools and to utilize data collected by CIM systems in support of process synthesis. We have demonstrated the application of the system to process parameter determination, simulator tuning, process control modeling, and statistical process optimization. We are extending the system to more fully support emerging device design and process synthesis methodologies. >

DOE/Opt: a system for design of experiments, response surface modeling, and optimization using process and device simulation

We have added the effects of strain (or stress) into 2DP [ IBM J. Res. Dev. 29 , 242 (1985)], a drift-diffusion simulator. The transport equations are modified to account for strain effects in such a way that few source code changes are required for easy implementation in any drift-diffusion code. The electron transport model is modified to include the predominant strain effect of shifts in the ellipsoid minimums of the band structure. The effect of strain on hole transport is modeled through changes in the hole concentration and an angular, averaged change in the mobility. The models are verified and a good match for the calculated piezoresistance coefficients of silicon are obtained with those from experiment. Then, we examine the effects of strain on npn transistors caused by isolation trenches. The calculated Gummel plots and ΔV be match well with experimental data.

Strain effects on device characteristics: Implementation in drift-diffusion simulators

An overview is presented on the types of problems encountered in semiconductor technology development that are actively studied today via simulation methods Most of the simulation examples presented here are ones that have been explicitly used in actual industrial semiconductor device design cycles to aid in the optimization of device structures The examples described here include process simulations, such as the diffusion of dopant atoms, oxidation, etching, deposition, and epitaxial growth, as well as device simulations, which predict the flow of charge carriers and field distribution within a semiconductor device, given its material structure and operating conditions The main aim here is to illustrate, by example, some of the capabilities of state-of-the-art simulators used in characterizing and predicting semiconductor process and device-related phenomena We will attempt to outline the degree of sophistication of the physics incorporated in such simulation programs, and provide some contrast to the fundamental physics required for a complete physical description As will be indicated, simulation development necessarily involves molding the appropriate physical models and numerical algorithms into a package that can be handled in a reasonable length of time by modern computing systems We briefly outline some of the advances that have been made, and some concerns that remain, in such simulation development

D. Katcoff

Papers

The use of simulation in semiconductor technology development