Victory Advanced Structure Editor. 3D Process Simulator for Large Structures

Transcription

1 Victory Advanced Structure Editor 3D Process Simulator for Large Structures

2 Applications Victory Advanced Structure Editor is designed for engineers who need to create layout driven 3D process based structures for subsequent accurate device simulation 3D simulation of individual cells and devices MOSFET FinFET SRAM 3D simulation of large, high feature density cells (with many implant/ anneal and epitaxy steps) single TFT cell, CMOS photo cell TFT, LED cells, CMOS RGB photo cells MEMS - 2 -

3 Benefits Process simulation based 3D device builder Solve accurately critical 3D process steps(e.g. doping, thin layer deposition) Increase productivity with fast 3D process simulations Optimize device performance as a function of process parameters Solve process integration issues due to layout design errors Seamless link with state of the art 3D Device simulators High level of automation (integrated in the VWF) - 3 -

4 Advantages Fast prototyping, including use of GDSII layout data Accurate 3D process simulation Simple, intuitive SUPREM-like syntax Input file follows the real process flow Versatility Tiny detailed structures (e.g. disk heads, vias) Large Structures (whole image sensor cells) Links to Atlas3D or Victory Device simulators Unique capabilities (large-scale MC Implant and diffusion, high-aspect ratios and thin layers) Powerful gridding modes for device optimized structures Using established DeckBuild environment - 4 -

5 Victory Advanced Structure Editor Victory Advanced Structure Editor uses application specific meshing and numerical methods and provides smooth synchronization of data between process simulation modules This flexible approach Victory Advanced Structure Editor to simulate effectively large sized 3D structures - 5 -

6 Tracking Fronts in Etch/Deposition - Etch and Deposition Victory Advanced Structure Editor simulates realistic geometric etch and deposition steps very efficiently with unstructured tetrahedral mesh - the developed algorithm combines the efficiency of string methods and the robustness of Level Set methods A generic model for etch and deposit Illustration for one step of the moving front; r iso (p) is isotropic etch rate at point p, and r dir (p1) is directional etch rate at point p

7 Tracking Fronts in Etch/Deposition - Example Creation and refill of a trench structure Patterning of photoresist. Directional etching; r dir is determined by the visible cone from above

8 Tracking Fronts in Etch/Deposition - Example Creation and refill of a trench structure Trench refill; illustration of void creation. Final trench structure with mesh

9 Tracking Fronts in Etch/Deposition - Example Creation of part of an electrostatic MEMS Part of a MEMS actuator array. MEMS device after isotropic release etch

10 Tracking Fronts in Etch/Deposition - Example Creation of part of an electrostatic MEMS Mesh at a corner without post-processing. Mesh at a corner with post-processing

11 Tracking Fronts in Etch/Deposition - Summary Robust algorithm based on the adaptive refinement of a tetrahedral grid Combines the efficiency of the string method and the robustness of the level set method Successfully solves some difficult problems such as de-looping and void creation Fast, typical times are from few seconds to couple of minutes

12 Immersed Boundaries in 3D Diffusion Diffusion and Annealing For dopant redistribution and activation, Victory Advanced Structure Editor uses the finite difference method, which is highly effective for numerical solution of systems of partial differential equations in complex geometries High performance solutions on irregular Cartesian meshes facilitating local refinement Immersed boundary grid to handle material interfaces

13 Immersed Boundaries in 3D Diffusion Immersed Boundary Grid Easy accessible data for structured nodes Special storage for interface nodes Possibility of grid refinement according to structure s geometry and active regions material boundary with interface nodes structured nodes

14 Immersed Boundaries in 3D Diffusion Finite difference method conservative monotonic approximation scheme splitting technique is allowable and results in crucial speedup of the calculations Efficient linear/nonlinear solvers space matrix structure is taken into account according to number of dopants/equations Time integration one-step Euler scheme step size control algorithm Diffusion Data Structure (DDS), 2D cross-section

15 Immersed Boundaries in 3D Diffusion 3D diffusion and annealing example showing doping in CMOS image cell

16 Immersed Boundaries in 3D Diffusion - Summary Fully compatible with Fermi diffusion model in Athena Solid solubility and impurity segregation at material interfaces Concurrent simulation of several impurities Allows diffusion simulation in structures with up to 4 million nodes in less than 1 hour using standard diffusion models 10 to 1000 times faster than other diffusion simulation methods

17 Monte-Carlo Ion Implantation 3D Monte-Carlo Ion Implantation in Crystalline Silicon Physically based model Feature dependent adaptive mesh Hierarchical level of acceleration techniques

18 Monte-Carlo Ion Implantation Physically-Based Simulation Model Realistic treatment of: i. Nuclear stopping well calibrated inter-atomic potentials for silicon. ii. iii. Electronic stopping, i.e. inelastic energy loss local and non-local stopping models. Amorphyzation damage buildup and accumulation. Amorphous pockets. ion e - e - electronic stopping e - lattice nuclear collisions

19 Monte-Carlo Ion Implantation Comparison with Molecular Dynamic Simulations Example 200 ev Boron into 100 Silicon G. Hobler, G. Betz (Inst. f. Allg. Physik, TU Wien) 2D slice of Victory Advanced Structure Editor MC Implantation

20 Monte-Carlo Ion Implantation The Monte Carlo module takes into account all important implantation effects: ion channeling ion dose dependency multilayer effects partial shadowing of ion flux multiple ion reflections scattering from mask walls Some of these important effects are most pronounced in case of angled implant into 3D structures

21 Monte-Carlo Ion Implantation - Ion Distribution in Silicon Primary, i.e. direct impact implantation Shadowed, i.e. secondary impact implantation

22 Monte-Carlo Ion Implantation Shadowed doping could be up to 10% of primary doping distribution primary secondary

23 Monte-Carlo Ion Implantation - Summary Method is inherently accurate for all applications Flexible mesh and combination of acceleration techniques allows simulation equivalent of few million trajectories in 1min 1hr depending on ion mass/energy and mask layout

24 Case Studies Layout based MOSFET simulation Layout driven Image Sensor simulation Well-proximity effect (pending) SRAM (pending)

25 MOSFET Simulation Physical layout and part of the input deck process flow go VictoryAdvancedStructureEditor! option run.full! init layout="dk_50nm.lay" gasheight=2 depth=1 padding=0.1! cartesian mask= ISO! cartesian mask= POLY! cartesian...! cartesian line zdir location= spacing=0.02! cartesian line zdir location=-0.25 spacing=-1! cartesian line zdir location=-0.205! cartesian...! mask "ISO" reverse! etch silicon thick=0.5 max! doping silicon boron=1e15! implant boron energy=5 dose=1e14 bca n.ion= ! strip resist! deposit oxide thick=0.005 max! implant boron energy=5 dose=1.5e13 bca n.ion= ! implant boron energy=30 dose=2e13 bca n.ion= ! mesh prism file="trench.str"! deposit poly thick=0.1 max! implant phosphor energy=20 dose=5e15 bca n.ion= ! mask "POLY"! etch poly! strip resist! mesh prism file="poly.str"! deposit nitride rate=1 time=0.1 isotropic=1 dl=0.04! etch nitride rate=1 time=0.11 isotropic=0!

26 MOSFET Simulation The polysilicon gate, spacer and electrodes of the final MOSFET simulated cell. Cross section along source-drain electrodes

27 MOSFET Simulation The MOSFET cell showing electron density distribution. Sub-threshold IV characteristics for different V t implant doses

28 Image Sensor Simulation Input Files 3D Process Simulation Run Time 3D Device Simulation Run Time Prototyping Summary Adding Photolithography Simulations Adding 3D Monte-Carlo Simulations FDTD Cross Section Recombination

29 Image Sensor Simulation Input Files Layout GDSII or Silvaco Layout Format (MaskViews) Simple Image Sensor Layout Example

30 Image Sensor simulation Input Files For fast prototyping and de-bugging, use automatic mask generated XY gridding and geometric deposit/etch to create the structure Automated XY meshing from mask layout Familiar SUPREM syntax DeckBuild environment

31 Image Sensor Simulation - 3D Process Simulation Run Time And by Fast we mean a prototyping runtime in minutes! 8 x - 3D Masking steps 4 x - 3D Implantation steps 3 x - 3D Diffusion steps 4 x - 3D Deposition steps 4 x - 3D Etch steps Total 3D Process Simulation Time on 2.83 GHz machine 2 Minutes 14 Seconds! (on a single CPU!)

32 Image Sensor Simulation - 3D Device Simulation Run Time 3D Device Simulation for 3 transients 1 CPU GHz (Including light Exposure) 29 minutes!

33 Image Sensor Simulation - Prototyping Summary Very fast simulation times Allows quick de-bugging of mask layouts Allows quick de-bugging of process flow Allows quick look-see 3D device simulations Standard specification desk top computer can be used Once the fast prototyping simulations have verified that the Layout and Process Simulations are correct, more detailed simulations can be done. e.g., Photolithography, Monte-Carlo Implants etc

34 Image Sensor Simulation - Adding Lithography Simulations Adding Photolithography simulation: Step 1 - Choose which masks are associated with implants Step 2 - Simply add mesh to each implant mask edge using the automated mask spacing feature

35 Image Sensor Simulation - Adding Lithography Simulations Step 3 Simply add Litho to the Mask Statement e.g., Mask POLY litho image= intensity.str Photoresist Optical Intensity during Polygate Mask Exposure

36 Image Sensor Simulation - Adding Lithography Simulations Poly Gate Photoresist Pattern after Lithography Showing optical effects of diffraction and interference on the final photoresist pattern

37 Image Sensor Simulation - Adding Lithography Simulations 3D Structure All 8 Masks using Lithography Simulation Plus additional Mesh Simulation Time 4 mins. 45 seconds

38 Image Sensor Simulation - Adding 3D Monte-Carlo Implant Simulations All 4 Implants using 4 Million Trajectories for Each Implant Total process simulation time 2 hours 8 minutes Showing shadowing of the photoresist for the high angle implants and scattering effects

39 Image Sensor Simulation FDTD Cross Section Simulation Full Exposure Showing Z-Magnetic Field Components and Photo-generation

40 Image Sensor Simulation - Recombination 3D Recombination Rate from Photo-Generation Targeted light exposure only in Image Sensor active region

41 Conclusion Very versatile Process and Device 3D simulators Can be used for fast prototyping of large structures or detailed analysis of intricate details Several Gridding Modes available to the user User definable models 3D process simulation, not just 3D structure editing Simple, intuitive SUPREM syntax Input file follows the real process flow Unique capabilities (large-scale MC Implant and diffusion, high-aspect ratios and thin layers) Silvaco is committed to continuous development of this core product working with customers to add still more capability