Bossung curves: an old technique with a new twist for sub-90-nm nodes

Zavecz, Terrence E.

doi:10.1117/12.656642

Cited by 10 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To detect the presence of early haze levels or lens surface contamination the data must be measured and analyzed as a dose-uniformity analysis, this technique was first described in an earlier paper by the author. 4 Depending upon the cause of reticle haze formation, haze can form on arbitrary sections of the reticle as shown in figure 2. This does not mean that, as in an image-direct analysis, all sites on the reticle must be measured.…”

Section: Experimental Setup For Reticle Haze Detectionmentioning

confidence: 99%

Rapid and precise monitor of reticle haze

Zavecz¹,

Kasprowicz

2007

SPIE Proceedings

View full text Add to dashboard Cite

Reticle Haze results from the deposition of a chemical residue of a reaction that is initiated by Deep Ultra Violet (DUV) or higher frequency actinic radiation. Haze can form on the backside of the reticle, on the chrome side and on the pellicle itself 1 .The most commonly reported effect of haze is a gradual loss in transmission of the reticle that results in a need to increase the exposure-dose in order to maintain properly sized features. Since haze formation is non-uniform across the reticle, transmission loss results in an increase in the Across Chip Linewidth Variation (ACLV) that is accompanied by a corresponding reduction in the manufacturing process window 2 . Haze continues to grow as the reticle is exposed to additional low wavelength radiation through repeated use.Early haze formation is a small-area phenomenon in comparison to the total area of the reticle and may initiate simultaneously in separate areas. The early stages of reticle haze therefore results in a degradation of Best Focus, Depth of Focus and the Exposure latitude of individual features in the "hazed" area prior to any noticeable large area transmission loss. Production lots subject to reticle hazing on critical layers will experience a direct loss of lithographic yields, loss of capacity, an increase in rework rates and an ultimate loss in overall final-test yield long before the need for an overall image exposure-dose increase is detected.Feature profiles and process response are degraded at the earliest stages of haze formation. While early hazing may occur in a small area of the reticle, the area influenced by the initial deposition is relatively large in comparison to the size of an individual circuit feature. A sampled metrological inspection of a regular array of points across the exposure field is therefore able to detect any form of reticle haze if the analysis monitors the feature-profile response rather than simply feature widths. A model-driven method for the early detection of reticle-haze using basic feature metrology is developed in this study. Application results from a production reticle are used to demonstrate validation of the technique that employs a highly accurate method of calculation of the uniformity of the reticle exposure-response for individual features across the exposure. INTRODUCTIONReticle haze is a problem that occurs in DUV and lower wavelength environments. Reticle haze was initially observed as a reticle back-side phenomenon but has recently been recognized as a contaminant that can appear on the chrome or 'object side of the reticle. Haze is a contaminant that is caused by mask-making chemicals, process residue, reticlecontainer out-gassing or through localized high-energy interactions with environmental gases in the exposure tool environment.Reticle haze on DUV process tools is rising in significance as a significant source of yield and capacity loss in device manufacturing. Reticle front or back-side haze causes non-uniform transmission loss within and near the hazed-areas across the reticl...

show abstract

Section: Experimental Setup For Reticle Haze Detectionmentioning

confidence: 99%

Rapid and precise monitor of reticle haze

Zavecz¹,

Kasprowicz

2007

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…In addition to RMS, we introduced Bossung Curve Error (BCE) 6 as one of the objective functions of collaborative optimization, and our GA algorithm is committed to minimizing BCE and RMS to achieve a sufficient balance. Bossung curve delivers several additional response surfaces needed for proper qualification of any exposure tool and reticle set.…”

Section: In-house Genetic Algorithm For Collaborative Optimizationmentioning

confidence: 99%

“…The classic Bossung curve analysis is the most commonly applied tool of the lithographer. 6 The analysis maps a control surface for critical dimensions (CDs) as a function of focus and exposure (dose) variables. Most commonly the technique is used to calculate the optimum focus and dose process point that yields the greatest depthof-focus (DoF) over a tolerable range of exposure latitude.…”

Section: In-house Genetic Algorithm For Collaborative Optimizationmentioning

confidence: 99%

Co-optimization of optical and resist models in the OPC modeling process using in-house genetic algorithm

Pan¹,

Wang²,

Chen³

et al. 2023

DTCO and Computational Patterning II

View full text Add to dashboard Cite

Traditional modeling of computational lithography starts first by determining the functional relationship between the change in focus and the aerial image (AI) location of the optical model by setting constraints and then calibrating the resist model separately. In this process, built-in genetic algorithm (GA) tools usually participate in the parameter optimization process of only one model at a time. Additionally, GA tools are vulnerable to becoming trapped in a locally optimal solution. The practice of optimizing the optical and resist models separately may potentially miss better solutions. We propose a method to co-optimize the two models simultaneously. This is done by finding the Pareto optimal frontier of potentially better solution candidates that balance these two models. To avoid the local optimal solution trap, a method is proposed to increase the search range when the algorithm is confined. In the selecting and scoring models process, we quantify metrics that are typically made empirically by engineers to achieve higher levels of automation.

show abstract

“…By the Bossung curves [27], the PV band will be improved by the improved defocus after SRAF insertion. In PVOPC, we give a systematic SRAF insertion algorithm for automatically placing SRAFs and determining their sizes.…”

Section: )mentioning

confidence: 99%

Fast lithographic mask optimization considering process variation

Su¹,

Huang²,

Tsai³

et al. 2014

2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

View full text Add to dashboard Cite

As nanometer technology advances, conventional OPC (Optical Proximity Correction) that minimizes the EPE (Edge Placement Error) at the nominal corner alone often leads to poor process window. To improve the mask printability across various process corners, process-window OPC optimizes EPE for multiple process corners, but often suffers long runtime, due to repeated lithographic simulations. This paper presents an efficient process-variation-aware mask optimization framework, namely PVOPC (Process-Variation OPC), to simultaneously minimize EPE and PV (Process-Variation) band with fast convergence. The PVOPC framework includes EPE-sensitivity-driven dynamic fragmentation, process-variation-aware EPE modeling, and post correction with three new EPE-converging techniques and a systematic sub-resolution assisted feature insertion algorithm. Experimental results show that our approach efficiently achieves high-quality EPE and PV band results.

show abstract

Bossung curves: an old technique with a new twist for sub-90-nm nodes

Cited by 10 publications

References 8 publications

Rapid and precise monitor of reticle haze

Rapid and precise monitor of reticle haze

Co-optimization of optical and resist models in the OPC modeling process using in-house genetic algorithm

Fast lithographic mask optimization considering process variation

Contact Info

Product

Resources

About