Atomistic Modeling of Complex Silicon Processing Scenarios
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Atomistic Modeling of Complex Silicon Processing Scenarios Martin Jaraiz, Pedro Castrillo, Ruth Pinacho, Lourdes Pelaz, Juan Barbolla, George H. Gilmer1 and Conor S. Rafferty1 Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain. 1 Lucent Technologies Bell Labs, 600 Mountain Ave., Murray Hill, NJ 07974, U.S.A. ABSTRACT The level of sophistication reached by today's Si device fabrication technologies has called for new modeling and simulation schemes, capable of handling the wide variety of interaction mechanisms that govern the complex phenomena that can occur at the atomic level. The kinetic Monte Carlo (KMC) technique seems particularly apt for this task. It takes as input basic materials parameters, derived from ab-initio calculations or from experiments, and is capable of carrying out a detailed simulation up to the dimensions and time scales of current ULSI Si device manufacture. In addition, it can accommodate and efficiently simulate complex interactions between multiple dopant and defect types. We explain the approach and show examples of application in both materials processing and device fabrication. Finally, we present the use of some artificial intelligence techniques (namely, genetic algorithms) that look most promising as methodologies that can easy and efficiently be employed to build the extensive KMC parameter database. INTRODUCTION As a result of the efforts to make progress into the deep submicron IC technology, silicon processing is facing an increasing level of complexity. And this situation can only worsen as it is pushed closer to the limit of its possibilities, limit that is expected to be reached around the end of this decade. Front end processing, in particular, is trying to extend the use of conventional and well established doping techniques (ion implantation plus furnace/RTP annealing) into this size regime (quarter-micron to deca-nanometer). However, many new effects show up when those techniques, well suited for larger feature sizes, are applied to sub-quarter micron processing. Since many of these effects are concurrent, the interpretation of experiments becomes ambiguous and the use of predictive process simulation becomes almost imperative. Atomistic Kinetic Monte Carlo (KMC) process modeling seems especially apt to fulfill these needs and is, thus, emerging as a most valuable simulation tool in the forefront of advanced materials processing research. THE PROBLEM: COMPLEX PROCESSING SCENARIOS Figure 1 shows a cross-sectional view of the simulation of a typical step in current front end processing: the annealing of a low energy (5keV) As implant to form the source/drain extensions of an N-channel MOS transistor. For that simulation to be truly predictive, physical modeling becomes essential because:
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1. There are many different species present: Vacancies (V), self-interstitials (I), arsenic, boron, native carbon and oxygen. 2. There are many different interactions: I-I, I-B, I-C, V-O, C-I-O, … 3. Highly non-equilibrium conditions: P
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