Effect of open area ratio and pattern structure on fluctuations in critical dimension and Si recess

Abstract

The authors quantitatively investigated the effects of open area ratio and pattern structure on fluctuations in critical dimension (ΔCD) and Si recess depth (ΔdR). To model these effects, under the assumption that three factors—mask open area ratio at the wafer level (global), chip level (semi-local), and local level (local)—affect ΔCD and ΔdR, they performed experiments using wafers ranging from 0.60 to 0.91 of the global range (RG) and the semi-local range (RS) treated by the HBr/O2 plasma etching process, where photoresist mask patterns on the poly-Si film with solid angles (ΩL) ranging from 0.2π to 0.9π were located. As a result, the authors found that ΔCD had positive and linear correlation with the RG value, which was consistent with the trend of the integrated intensity of the etched by-product (SiBrx) estimated by optical emission spectroscopy data and with that of taper angles of observed etched profiles. They also clarified that ΔCD was affected by the amount of SiBrx generated within several times of the mean free path area for the semi-local dependence and that the ΩL value within a 2 μm area, not the pattern space, had a good correlation with fluctuations in ΔCD as a control indicator. Using this experimental knowledge, the authors developed a quasi-three-dimensional Si gate etching simulation procedure that demonstrates the ΔCD value and the etched profile characteristics. Furthermore, our simulation procedure found that ΔdR caused by ion bombardment in the Si substrate, as well as ΔCD, deeply depended on the (RG + RS)ΩL factor. Taking account of the relationship between dR and the ion energy reduced by the SiBrx deposition depth on the Si substrate, the authors found that dR was dependent on the factor, which was consistent with experimental data. These results show that for improving device performance, it is crucial to consider the effect of (RG + RS)ΩL on ΔCD and ΔdR in controlling plasma etching parameters, such as ion energy, gas flux, and etching time.

Advanced simulation technology for etching process design for CMOS device applications

“…[93][94][95][96][97][98][99][100] These fluctuations have also been confirmed experimentally in the etching of other films, such as SiO 2 101) and Al, 40) with different by-products being generated from the etched surfaces that depend on the pattern density (for both dense and isolated patterns). These fluctuations caused by the byproducts have presented a significant issue to be resolved to realize high device performance levels and high production yields.…”

“…R G = 0.72 3.2 Incident flux of by-product By considering the generation of SiBr x by the etching process and losses due to dissociation in the bulk plasma, the pumping of the chamber, and plasma-chamber wall interactions, the density of SiBr x , N SiBrx , is obtained from the differential equation as follows: 95) dN…”

“…D G and D S represent the dissociation rates for by-products originating from the global and semilocal ranges, respectively. In addition, assumptions that D G ∼ D S = D and ΔCD ∝ Γ SiBrx lead to the following equation: 95) ÁCD…”

“…The etched profile can be predicted in a quasi-2D space 95) by including Eq. (13) in the surface reaction of a standard string model.…”

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

“…This simulation result indicates that over-etching time should be optimized in the view of not only feature scale profile but also amount of Si damage to realize high performance of the CMOS devices. In another study, Kuboi et al 23 formulated the incident fluxes of byproducts (SiBr x ) dependent on open area ratios of wafer/chip/pattern scales during planer FET Si gate etching with HBr∕O 2 plasma using the CCP reactor analyzing experimental data, which was adopted to the string-slab model. The flux was formulated by E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 7 ; 2 4 5 Cooperberg et al 26 calculated incident gas fluxes of Cl 2 ∕BCl 3 plasma as input parameters for the feature scale profile using the Hybrid Plasma Equipment Model (HPEM) code 27 and predicted mask facets, sidewall deposition distributions, and microloading of the Al etched profiles in the iso/dense regions depending on etching process conditions such as top power (300/ 500/800 W), bias power (100/200/400 W), and etching time (60/45/42 s).…”