On the trends and application of pattern density dependent isofocal dose of positive resists for 100 keV electron beam lithography

Abstract

This work examines the isofocality of four commercially available positive resists for electron beam lithography (EBL) at 100 keV: AR-P 6200 (commercially known as CSAR 62) by AllResist GmbH, ZEP520A by Zeon Corp., polymethylmethacrylate 950 A4 (950k molecular weight in anisole) by MicroChem Corp., and mr-PosEBR 0.3 by Micro Resist Technology GmbH. Isofocality is the operating point in a given process where a specific dose (namely, the isofocal dose) results in the same feature size (isofocal feature) independent of the effective blur (blureff). The blureff is a lumped parameter that includes the effects of resist processing, spot size, beam focus, forward scattering, etc., which contributes to the final resist image. The isofocal feature is typically larger than the drawn target critical dimension (CD). The difference between the isofocal feature size and the CD target defines the isofocal bias. By analyzing the exposure latitudes across 0%, 25%, 50%, 75%, and 100% pattern densities (ρ) with feature sizes ranging from 100 to 400 nm, the approximate pattern density dependent isofocal doses (IFDρ) and isofocal biases (IFΔρ) are identified for a silicon substrate across all four resists given their fixed processes at 100 keV. Examining the trends in isofocality in these positive resist processes, the proximity effect correction is adjusted to provide the empirically found IFDρ for 100 keV EBL on a silicon substrate.

Optimization of Quantum-dot Qubit Fabrication via Machine Learning

“…Approaches to address proximity effects to date have relied predominately on measuring linewidths of dedicated monitor structures. 52,53 We now demonstrate an alternative approach that leverages our machine learning algorithm to identify onset exposure doses as a function of local pattern densities naturally occurring in the leads of our devices. 54 The evolution of onset exposure dose D l with local pattern density ρ is traditionally described by a seriesexpanded Gaussian convolution model, 55…”