Correlation Model for Size Measurement of Nanoparticles by Inductively Coupled Plasma-Mass Spectrometry

A data treatment model for fast and reliable particle size measurement by single particle ICP-MS was developed in this work. The proposed approach is based on the correlation between the known size of nanoparticle vs. the peak area obtained by time-resolved analysis. The designed model showed sufficient reliability and speed for industrial applications depending upon the credibility of correlation curve and the accuracy of particle size. For this, a window method was designed, where the data range is selected to identify ideal peaks in time-resolved spectrum and the average peak area of the particles is estimated from parameter, σ, of its Gauss fitting of the relationship. The regression coefficient (R2) of the correlation curve for commercial Au nanoparticles of <0.999 was obtained in 5 repeated measurements. The method was confirmed using NIST Au standard nanoparticles and the results showed excellent correlation with a R2 of 0.9998. The size of unknown particles determined using the proposed method matched well with that determined using TEM: the values matched within ±12% and the relative expanded uncertainty was 26% for 10 repeated measurements. Furthermore, the developed method was applied to iron oxide (Fe2O3) nanoparticles used in semiconductor manufacturing and the result showed a correlation coefficient of >0.998 as the background subtraction level was adjusted to the mean of blank signal plus 3σ (standard deviation). Overall, the developed method successfully demonstrated great potential for the determination of the size of nanoparticle with high accuracy and speed, especially for on-line monitoring of various chemicals used in the manufacturing of semiconductors.

I. Introduction

Single particle inductively coupled plasma mass spectrometry (spICP-MS) has emerged as a characterization tool for metal-containing nanoparticles in various fields, such as food [1], [2], bio [3], [4], and environmental applications [5]. In semiconductor manufacturing, spICP-MS is particularly promising for particle monitoring to prevent the manufacturing process from circuit/chip failure. However, its practical application is difficult because the small numbers of particles in clean environments and the requirement of high speed with accuracy and sensitivity: currently spICP-MS shows particularly poor reliability for on-line monitoring [6], i.e., for fast and reliable monitoring of a small number of particles with small size. The reason is that typical spICP-MS needs time to obtain indispensable information on sample uptake rate and nebulization efficiency [7]–[9]. In addition, some nanoparticles do not have a perfectly round shape and information of particle density is frequently unavailable. These parameters cause uncertain errors in size measurements when using the deconvolution algorithm of single-particle analysis, especially for trace measurements. Therefore, a more reliable and valid measurement method is required to make spICP-MS applicable to semiconductor chemicals and processes.