Simulation and characterization of surface and line edge roughness in photoresists before and after etching

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Jean Doyle
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1 Simulation and characterization of surface and line edge roughness in photoresists before and after etching Motivation of this work : Sub 100nm lithographic features often suffer from roughness. Need to understand its origins, characterize and reduce it. Purpose : Quantitative characterization of roughness Examine different roughness parameters and their interrelations and determine the best way of characterizing roughness. Dependence on material properties and process conditions (experimental study) Study of roughness dependence on material properties and process conditions. Simulation of roughness formation (prediction) Understand the Line edge and Surface roughness ( and ) formation and prediction with molecular simulations.

2 Origins of Surface and Line Edge Roughness ( and ). Materials Processes Material properties: Polymer, MW, MW distribution Si content Coating Exposure Energy PEB deprotection / crosslinking Exposure system characteristics ( ) (latent image formation) latent latent Wet development of top layer Silylation Wet development (isotropic) Dry development of bottom layer (Anisotropic) Option: breakthrough step Pattern Transfer (etching) BiLayer Dry Development (Anisotropic) Option: Breakthrough step Pattern transfer (etching) Silylated Pattern transfer (etching) Single layer

3 N 1/ s rms zi Zav N i 1 Characterization of roughness 1. Rms roughness easily calculated only gives the vertical magnitude of roughness depends on the scale of measurement 2. Fractal dimension D (calculated by the variation method) (B.Dubuc et al. Phys. Rev. A 39, 1500 (1989)) D=1.2 D=1.7 (for lines 1<D<2, for surfaces 2<D<3 ) needs careful implementation measures the spatial complexity of roughness.3. Scaling behavior of rms in an experimental surface Scaling hypothesis : where Rms(L) is an average over many samples, L cor is the correlation length and the scaling exponent 0<a<1, D=2-a Take care of the correlation length Lcor : The estimation of the Rms is reliable if and only i the sample size L is larger than the correlation length Lcor (L>Lcor).

4 4. Frequency spectrum (FFT analysis) 0,1 amplitude 0,01 1E-3 related to rms slope = fractal dimension D Power law in FFT reveals self-similar structure. Fractal analysis is possible 1E-4 0,01 0,1 spatial frequency (nm -1 ) High frequency FFT behavior gives the fractal dimension BUT needs more data points than variation method Low frequency FFT amplitude is related to rms BUT only qualitatively.

5 Negative tone epoxy resist (wet development) Negative tone siloxane resist (oxygen development in HDP reactor) Rms (nm) PAG 1%, PAB 110 o C Dose (µc/cm 2 ) 2.45 Fractal dimension D At high doses rms increases and D decreases Effect of exposure dose and bias voltage during BTS Rms (nm) no bias at BTS bias 100 V at BTS Dose (µc/cm 2 ) No bias voltage rms curve drops more quickly Fractal dimension D Opposite behavior of rms and D vs exposure dose

Gel Formation Modeling in a Negative Tone CAR CH3 O [ ] H2C CH2 CH O CH2 Polymer chain with 5 monomers Lattice Model of EPR (EPoxy Resist) Crosslinked Monomer Initiated Sites Crosslink Free Volume

6 Gel Formation Modeling in a Negative Tone CAR CH3 O [ ] H2C CH2 CH O CH2 Polymer chain with 5 monomers Lattice Model of EPR (EPoxy Resist) Crosslinked Monomer Initiated Sites Crosslink Free Volume Gel Formation (Molecular Modeling) Polymer chains and initiator molecules in lattice Initiation -Acid Diffusion - Cross-linking - Clustering Graph and Percolation theory Part of the same chain Periodic Boundary Conditions G.P.Patsis and N.Glezos, Molecular Dynamics Simulation of Gel Formation and Acid Diffusion in Negative Tone Chemically Amplified Resists, Microelec. Engin. 46, 359 (1999).

7 Line Edge Roughness Modeling after development 0.1µm 0.1µm 0.25µm C=10% in initiator RMS Roughness N 1/ s rms zi Zav N i 1 Line Edge Roughness Before and after exposure and after development Top and side line roughness Dependence upon polymerization length, acid diffusion length and initiator concentration Simulation of Surface and Line-Edge Roughness formation in Resists, G.P. Patsis, E. Gogolides, Microelectronic Engineering, (2001), 563-9

8 Rms Roughness (nm) Rms Comparison of simulation with experimental results after development 5%PAG 1%PAG Exp. data 1% PAG from crosslinks 1%PAG Dose (µc/cm ) (for of negative tone epoxy resist) Fractal dimension D 2,6 2,5 2,4 2,3 2,2 Fractal dimension D 1% PAG simulator (after crosslinks formation) experimental simulator (after development) 2,1 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 Dose (µc/cm 2 ) Qualitative agreement between simulation and experimental data Simulator reproduces the opposite behavior of rms and D vs dose

9 What more about D 1. Fractal dimension D and correlation length Lcor* Positive tone resist surfaces 2. Error in Rms measurements (SD) versus fractal dimension D Negative tone resist surfaces (EPR) D and Lcor are not independent quantities. For the surfaces we studied: as D increases Lcor decreases (despite the fluctuations, due probably to the statistics). Negative tone resist surfaces give similar results. Similar results from positive resists. Surfaces with low D show large Rms error (SD). Hence, more samples need to be measured.

10 Conclusions 1. Important for roughness characterization : a) Rms (vertical roughness) b) Fractal dimension D (spatial roughness) FFT gives both Rms and D but needs a lot of data points to be reliable. 2 Rms measurements require sample sizes larger than the correlation length Lcor. 3. Correlation length depends on the dose for both positive and negative tone resists as well as the PAG content. 4. Theoretically, D, Rms and Lcor are independent quantities. But, for the positive and negative resists we studied : a. D and Rms exhibit opposite behavior. b. The same is true for D and Lcor. 5. As Lcor increases (i.e. D decreases) more samples are needed for accurate Rms measurement. 6. Simulation methodology exists, and compares well with experimental trends. 7. Simulation needs to be applied for positive tone resists, with aqueous base development

11 Collaborative Work Needed in before and after etching 1) Create SEM image analysis and detailed evaluation software. SEM images needed, and analysis from metrology instruments for comparison and standardisation. Find the most important parameters for characterization. 2) Have for a couple of resists SEM pictures for series of different conditions (aerial image, process, etc) in order to evaluate process and tool effects on. (193 and / or 157). Have SEM pictures also after etc. 3) Couple 2 above with detailed information on chemistry of resist for simulation of formation and development. 4) What about sidewall surface roughness AFM Analysis of AFM files with our methodology and software possible. 5) after etching, and Ultra Thin resist film resistance. 6) Input needed on standard etch recipes for various resist schemes. (plasma chemistry, steps, duration). 7) Where is etch resistance of UTR resists most needed How does it affect Where should the UTR etching analysis focus

Surface and line-edge roughness in solution and plasma developed negative tone resists: Experiment and simulation

Surface and line-edge roughness in solution and plasma developed negative tone resists: Experiment and simulation G. P. Patsis, A. Tserepi, I. Raptis, N. Glezos, and E. Gogolides a) Institute of Microelectronics,