Книги2 / 1993 P._Lloyd,__C._C._McAndrew,__M._J._McLennan,__S._N

Another area of growing importance and concern is 3D simulations. While individual 3D simulations have been used often for both MOS and bipolar device studies, there is now a growing list of interacting device effects that require not only 3D analysis but often coupled boundary conditions (mixed mode) with circuit elements, ambient conditions such as temperature and even inputs to support sensor applications. The results presented in Section II concerning visualization of 3D bipolar results indicate state-of-the-art results in very largescale simulations. Over a period of seven years and three-and-a-half generations of Intel iPSC computers, we have explored algorithms that exploit coarse-grain parallelism for such computations. Here we will highlight recent trends based on both the 32-node Intel iPSC/860 at Stanford and experiences using the 520-node "Delta" machine at Caltech.

3D Device Simulation Using STRIDE

STRIDE is our 3D finite-volume device simulator that was envisioned as a parallel program from the outset [22]. The focus in this effort is on iterative linear solvers and preconditioning schemes, nonlinear strategies and convergence, and the interactions with domain decomposition. The results shown in Figure 10 and Figure 11 were computed using STRIDE based on advances in such parallel algorithms as discussed below.

Large-scale 3D simulation leads to linear systems of equation that are only tractable with iterative linear solvers. Many variants exist, but all with one common theme: they do not work unless they are combined with an appropriate preconditioner to improve the conditioning of the system. Over the past two years, significant progress has been made in this area. In particular the development of ILU-V has been a breakthrough [23]. High-level injection bias conditions that would be inaccessible previously, are manageable with this new preconditioner. In cases where previous schemes would converge, we see a reduction in the number of linear iterations by at least a factor of two. Key in the development of preconditioners for parallel systems is the way that the processor boundaries are handled. In our scheme, the preconditioning is can be done on a processor-block basis, which dramatically reduces communication and increases parallel efficiency, but only if the matrix is completely assembled including contributions across the processor boundaries.

High-Resolution Device Simulation Using PISCES-MP

PISCES-MP is a parallel version of our standard PISCES-2B code, based on version 9009. Here, the emphasis is on developing a programming paradigm for parallelization of socalled "dusty decks". The use of a specific variant of the SPMD approach (Same Program, Multiple Data) allowed us to create a parallel version of PISCES while only touching a limited fraction of the existing code while still achieving excellent speedups. This is important to preserve the knowledge base that is inherent in the standard version through more than a decade of intense industrial use.

The fact that PISCES-MP is a 2D simulator implies that the grid count will never be as massive as for the 3D code Stride. As a consequence, it is possible to continue to use direct linear solvers instead of the less robust iterative solvers. A completely new version of a distributed multifrontal solver was developed as the numerical heart of PISCES-MP, which allows us to run simulations with up to about 30,000 grid points with good numerical efficiency on the present Intel iPSC/860 machine using 32 nodes. An indication of the achieved speedups is given in Figure 13. In order to explore very-high-definition device CAD with grid counts in the 100000+ range, one would have to use more CPU's than the 32 available

linear solvers and preconditioners, and visualization are all common. Interactions are developing between collaborators that linear-algebra groups, application groups in various engineering domains, as well as hardware vendors.

5.Summary

This paper provides an overview of several aspects ofTCAD work at Stanford. We emphasize both the application-oriented approach being used as well as the key factors that we see as being critical to advancing the field. On the applications side we have looked at several different areas of technology development (ie high speed, high voltage and compound materials) as well as some of the new issues related to virtual/programmable factory. In the process of discussing these applications we also addressed some of the user and information model issues such as: geometry modeling, gridding, visualization and automation of simulation control. Looking at limiting factors, we have considered two aspects of particular importance--physical model calibration and parallel processing as a means to overcome computational bottlenecks of TCAD. On the side of calibration, the effective use of statistical simulations is emphasized as being of major importance. Parallel computation re-

quires careful attention to all aspects of the simulation tasks involved. We have presented results based on both "dusty deck" parallelization efforts as well as highly tuned parallel

3D codes. Finally, a library-based approach for building further parallel application codes is advocated.

Acknowledgement

The authors wish to thank a number of Stanford colleagues for numerous contributions including figures. Specifically, we thank: Dan Yang, Datong Chen, Zhiping Yu, Krishna Saraswat, Robert Huang, Aon Mujtaba, Chiang-Sheng Yao and Bruce Herndon. In addition,

the contributions of our industrial partners in Stanford's Center for Integrated Systems and specifically those of Dr. Don Scharfetter (Intel), Dr. Ed Farrell (IBM), Dr. Reda Razouk (National Semiconductor), Dr. Waguih Ishak (Hewlett-Packard), Dr. Tony Alvarez (Cypress), and Dr. Robert Doering (TI) are acknowledged. Finally, sponsorship from ARPA!

CISTO, Semiconductor Research Office, and Army Research Office are gratefully acknowledged.

References

[1]ACIS; Spatial Technology Inc., Boulder, CO.

[2]L. Bishop, U. Ravaioli, P. Fu, D. Yergeau, Z. Sahul, D. Yang, and R. Goossens, "HDF-Vset File Format for Visualization of Mesh-Based Simulation Data", Technical Report, Stanford University, Oct. 1992

[3]CFI Document Number TCAD-91-G-2, "Semiconductor Wafer Representation Procedural Interface (PI), "Version 1.0, July 1992.

[4]AVS; Advanced Visual Systems Inc., Waltham, MA.

[5]D. Yang, K. Law, and R. W. Dutton, "Optimal Moving Meshes for Process and Device Simulation," NUPAD IV (Numerical Process and Device Modeling Workshop) Digest, pp. 181-186. Seattle, May 31--June 1, 1992.

[6]"Meshbuild - A 2-D Mesh Generator," Integrated Systems Laboratory, ETH Zurich, Switzerland.

[7]B. Herndon, A. Raefsky, R. J.G. Goossens, and R. W. Dutton, "PISCES-MP - Adaptation of a Dusty Deck for Multiprocessing," Presented at NASECODE-VIII, May 19-22, 1992.

[8]R. W. Dutton, J. D. Plummer, Power Semiconductor Devices and Circuits, "Tool Integration for Power Device Modeling Including 3D Aspects," Edited by A. A. Jaecklin, Plenum Press, 1992.

[9]R. Goossens, S. Beebe, Z. Yu, R. W. Dutton, "An Automatic Biasing Scheme for Tracing Arbitrarily Shaped 1-V Curves," Submitted to IEEE Transactions on CAD.

[10]D. Chen, E. C. Kan, U. Ravaioli, C. Shu and R. W. Dutton, "An Improved Energy Transport Model for Hot-electron Device Simulation," IEEE Elec. Dev. Lett., vol. 13, no. 1, pp. 26-28, Jan. 1992.

[11]D. Chen, Z. Yu, R. Goossens, K-C. Wu, and R.W. Dutton, "Dual Energy Transport Model with Coupled Lattice and Carrier Temperatures," SISDEP'93 Digest, Vienna 1993.

[12]P. Dankoski, P. Gyugyi, and G. Franklin, "An Extensible Software Design Applied to Rapid Thermal Processing," MRS Spring Meeting, Symposium on Rapid Thermal and Integrated Processing, San Francisco, April 12-15, 1993.

[13]F. L. Degertekin, J. Pei, Y. J. Lee, B. T. Khurl-Yakub, and K C. Saraswat, "In-situ Temperature Monitoring in RTP by Acoustical Techniques," MRS Spring Meeting, Symposium on Rapid Thermal and Integrated Processing, San Francisco, April 12-15, 1993.

[14]R. Huang, and R. W. Dutton, "Experimental investigation and modeling of the role of extended defects during thermal oxidation", Submitted to Journal of Applied Physics.

[15]W. T. Wong, J. Y-C. Pan, and 1. D. Plummer, "A Virtual Factory-Based Environment for Semiconductor Device Development," Proceedings of the IEEE International Manufacturing Science Symposium (ISMSS), San Francisco, CA, June 15-17, 1992.

[16]Private communication, Dr. Don Scharfetter, Intel Corporation, concerning BSIMJr. tool.

[17]Collaboration with Dr. Ed Farrell, IBM Research, visualization data to be published.

[18]D. Chen, E. Sangiorgi, M. R. Pinto, E. C. Kan, U. Ravaioli and R. Dutton, "Analysis of Spurious Velocity Overshoot in Hydrodynamic Simulations of Ballistic Diodes," NUPAD IV (Numerical Process and Device Modeling Workshop) Digest, pp. 109-114, Seattle, May 31--June 1, 1992.

[19]S. Sugino C.S. Yao and R.W. Dutton, "Parallelization of Monte Carlo Analysis on Hypercube Multiprocessors and on a Networked EWS System," SISDEP '91 Digest, Vol. 4, pp. 275-284, Zurich, Sept. 1991.

[20]J. Bude et al., Phys. Rev. B, 45, p.10958, 1992.

[21]C-S. Yao, D. Chen, R. W. Dutton, F. Venturi, E. Sangiorgi, and A. Abramo, "An Efficient Impact Ionization Model for Silicon Monte Carlo Simulation," VPAD Conference Technical Digest, p. 42, May 1993.

[22]K C. Wu, G. R. Chin, R. W. Dutton, "A STRIDE Towards Practical 3D Device Simulation

-- Numerical and Visualization Considerations," IEEE Trans. CAD, Vol. 10, No.9, pp. 1132-1140, Sept. 1991.

[23]Z.-Y. Zhao, Q.-M. Zhang, G.-L. Tan, and J.M. Xu, "A New Preconditionerfor CGS Iteration in Solving Large Sparse Nonsymmetric Linear Equations in Semiconductor Device Simulation", IEEE Trans. on CAD, vol. 10, p.1432, 1991.

[24]N. R. Aluru, K H. Law, P. M. Pinksy, A. Raefsky, R. J.G. Goossens, and R. W. Dutton,

"A Finite-Element Formulation for the Hydrodynamic Device Equations," To be published Computer Methods in Applied Mechanics and Engineering.

[25]N. R. Alum, , "Space-time Galerkin/Least Squares Finite-Element Formulation for the Hydrodynamic Device Equations," 1993 International Workshop on VLSI Process and Device Modeling (1993 VPAD) Digest, pp. 16-17, Nara Japan, May 1415, 1993

Edited by F. Fasching, S. Halama, S. Selberherr - September 1993

An Integrated Design Environment for Semiconductors

P.A. Gough

Philips Research Laboratories,

Cross Oak Lane, Redhill, Surrey RH1 RHA, UNITED KINGDOM

Abstract

In January 1989 the Integrated Device Design Environment (IDDE) software was released to Semiconductor Development sites within Philips. This paper reviews the original goals that underpinned the design of the TCAD framework, how well in the intervening 4 years it has met these objectives, what lessons have been learnt and what issues, from an industrial perspective, are still outstanding. Further, this work attempts to show that usability is an important component of TCAD systems and deserves far more attention than it currently gets.

1.Introduction

The development of the Integrated Device Design Environment [1] (IDDE) was largely borne out of the frustration experienced by device and process engineers attempting to use, in combination, multiple simulation tools e.g., SUPREM III [2], SUPREM IV [3], COMPOSITE [4], CURRY [5], TRIPOS [6]. Such a range of software, originating from many sources has a number of problems. Specifically these are:

•Each program has its own form of input. Often this is text based via keywords and parameters. An engineer is therefore required to convert a conceptual image

of a device or process into a rather unforgiving syntax. Unsurprisingly such a conversion is a source of error[7] and imposes a high access threshold.

•To use several simulation programs requires fluency in a number of input languages. This imposes a substantial learn/relearn load on users, particularly those requiring casual use of such tools.

•Transfer of data between programs is often difficult. Users are constantly confronted with output from one program that is incompatible with another. Even when conversions do exist they tend to be rather ad-hoc being developed on an as needed basis.

The simulation tools had other difficulties, but those highlighted were identified as the main causes for impeding the integration of simulators into the device design methodology. This was particularly true for development areas as, in contrast to research, there is less opportunity to experiment with new approaches to design and a degree of impatience for a return on time invested in new methods. Therefore, it was clear that there were potentially large benefits to be gained through the development of a TCAD framework that incorporated both an enhanced interface and improved integration of the component simulators.

The Integrated Device Design Environment (IDDE) represents an attempt to produce a system for semiconductor design that addresses the problems stated above. That is, its prime purpose is to make simulators accessible to device designers and increase the efficiency of engineer/simulator interaction. IDDE is a workstation based system (originally Apollo based but recently migrated to X-Windows) that utilises the graphical capabilities of these machines to provide a highly interactive and, in comparison to text based alternatives, an intuitive interface to the user. The system additionally incorporates a number of standard interface files (c.f. the profile interchange format of Duvall [10D to improve interprogram communication and produce an open system that can assimilate future modelling programs. The systems is sufficiently general to be used in both research and development areas.

The following sections describe in some detail the components of the IDDE system, the underlying data structures, how well during the four years of it's use it has fulfilled its original purpose and what lessons for future systems can be drawn from this work.

2.IDDE

2.1Features

The major features of the IDDE system are:

2.1.1Generality

A wide variety of devices are studied within Philips. These range from sub-micron bipolar transistors, to high voltage power devices to new and novel structures the subject of research. A prime goal of the IDDE project was to ensure that such diversity could be handled within the environment and so the software should not be restricted to a class of devices.

2.1.2Integrated Framework

A number of standard files have been defined within IDDE and it is through these that the tools and simulators communicate. These features lead to an open system with the following benefits:

•By conforming to an agreed standard new tools and simulators can be efficiently integrated into the system.

•It provides a basis for design automation.

•Remote sites can easily exchange data even if they are using different simulators.

2.1.3Graphical interfaces

An area in which IDDE differed from the contemporary environments was in em-

phasising an object-based graphical interface supporting direct manipulation (other systems have since appeared [llD. The main features of the interface are:

•The interface is highly graphical. For example a device is represented in schematic form familiar to designers. It is our experience that such graphics are mandatory if the system is to be "engineer friendly" .

•The use of direct manipulation techniques. Although a costly paradigm to implement [8] the direct interaction with objects is considered superior (see empirical studies [12] [13]) to a menu based systems.

•Icons and Menus support other aspects of the user dialogue.

2.2Architecture

The basic architecture for IDDE is shown in Figure 1. The arrows show the principle direction of data flow and the bold boxes are the main software components developed for IDDE.

Graphical

Viewer

Device

Simulator

Mesh

Editor

I

I

: I l.Q~~~s~ ____ J

Figure 1: IDDE Architecture

A typical scenario for the system's use would be the generating of a number of ID, 2D doping profiles and perhaps oxide regions which are stored in the database in a standard results format. A device can then be defined interactively using the regions and doping profiles previously created. A mesh can then be defined over the device, again interactively. The device and mesh data can then be translated into a variety of inputs for device simulation. The output from all of these simulators can then be viewed using the graphical viewer.

Integrated within the current version of IDDE are the process simulators: SUPREMIII, SUPREM-IV, COMPOSITE and the device simulators: HECTOR [14], CURRY, PISCES [15], TRIPOS, PADDY [16] and TESSA [17]. The components of the system are described in the following sections.

2.2.1Device Editor

The device editor allows the user to define the geometry and physical properties of a device in 2D and 3D. This can be viewed as a tool for manipulating a hierarchical data structure that represents the device. If our original aim of generality is to be met then this representation must be sufficiently flexible to accommodate arbitrary structures.

In IDDE a device is represented as a series of overlapping regions. These regions are composed of one or more shapes (termed segments) which have a particular material type (contact, dielectric, semiconductor). Each region or segment can have associated with it an arbitrary list of attributes that characterise it's physical properties (e.g. bandgap, mobility model, permittivity). A schematic representation of this decomposition is shown in Figure 2. In 2D the shape of a segment is given as a polygon. In 3D a combination of extruded polygons is used.

//1

/1

//1

/1

//1

Region

Figure 2: Device Representation

As overlapping regions are permitted a method of resolving conflicts is required i.e. when items overlap which object dominates. An item's position in the definition list is used to determine this, the region defined later is selected (the so called "painters algorithm").

Figure 3 shows a typical screen from the device editor. This consists of a central schematic image of the device with different regions (e.g. silicon, aluminium) denoted by colour (for purposes of this paper these have been converted to grey shades) with the junctions also delineated. Via the surrounding menus, various passive (e.g. zooming, panning) and active (e.g defining a new region) functions can be activated. More device specific aids are a probe that reports doping at a point in the device and the facility to view the doping profile along an arbitrary line defined by the user; in addition, a user can extract quantities such as sheet resistivities and gummel numbers from this line.

Direct manipulation is the main instrument of interaction. A user clicks on a region and it is highlighted and a panel activates giving information e.g. region type, attributes. The user can then, using the mouse, pick the object up and move it, stretch it and rotate it, essentially adopting a paradigm close to that used in object based