Highly spatial-resolved chemical metrology on latent resist images

Patterning photoresist with extreme control over dose and placement is the first crucial step in semiconductor manufacturing. But, how to accurately measure the activation of modern complex resists components at sufficient spatial resolution? No exposed nanometre-scale resist pattern is sufficiently sturdy to unaltered withstand inspection by intense photon or electron beams, not even after processing and development. This paper presents experimental proof that Infra-Red Atomic Force Microscopy (IR-AFM) is sufficiently sensitive and gentle to chemically record the vulnerable-yet-valuable lithographic patterns in a chemically amplified resist after exposure, prior to development. Accordingly, IR-AFM metrology provides the long-sought-for insights in changes in the chemical and spatial distribution per component in a latent resist image, both directly after exposure as well as during processing. With these to-be-gained understandings, a disruptive acceleration of resist design and processing is expected.

How to use and how not to use certified reference materials in industrial chemical metrology laboratories

As a producer of certified reference materials (CRMs), NIST faces high demand for Standard Reference Materials (SRMs). The demand is exacerbated by widespread misuse of CRMs. When should one use CRMs? When should one not use CRMs? Must labs always use NIST SRMs? How can labs demonstrate analytical capabilities for their accreditation scopes? Why so many questions? Standards developers, laboratory accreditors, and laboratory staff must be able to understand these topics with respect to quality systems in compliance with ISO/IEC 17025. They must calibrate and validate test methods and document traceability to the International System of Units (SI). Many people working in laboratory accreditation and under the umbrella of a quality system do not fully understand what these things are, let alone the language of chemical metrology. On average, they have little training in analytical chemistry, elemental analysis, and reference material development. It is hoped this paper will impress upon the reader the need for understanding how CRMs can be best used in the laboratory. This paper provides a brief background on the above problems and then looks at some of the support and reference information provided by NIST to metals and mining industries labs, commercial CRM producers, and accrediting bodies. The concepts and guidance apply broadly to chemical metrology and fundamental analytical chemistry. The paper includes examples (some from X-ray fluorescence spectrometry) to illustrate concepts.

Calibration

In the context of chemical metrology, calibration is the process of relating a known quantity of an analyte to the corresponding measured instrumental response through a mathematical relationship. Calibration permits the assignment of analyte levels in unknown samples based on the known levels of the calibrants. Details of the calibration model are important to achieve accurate results. Several common approaches are used in calibrating methods. Most frequently, calibration models are based on linear instrumental response, with mathematical models that include zero intercept, fixed intercept, unconstrained (fitted), and bracketed models. When instrumental response is nonlinear, a linear model may still provide accurate results if the calibration range is sufficiently limited. This presentation will provide an overview and application of various calibration models, with recommendations of ways to improve measurement accuracy. Examples are presented that illustrate advantages and disadvantages for each of these models as applied to low level samples and to unknowns with levels that span several orders of magnitude.

Quantifying Uncertainty in Analytical Measurements

In providing chemical, biochemical and agricultural materials testing services for quality specification, the analytical chemists are increasingly required to address the fundamental issues related to the modern concepts of Chemical Metrology such as Method Validation, Traceability and Uncertainty of Measurements. Without this knowledge, the results cannot be recognized as a scientific fact with defined level of acceptability. According to ISO/IEC 17025:2005, this is an essential requirement of all testing laboratories to attain competence to test materials for the desired purpose. of these three concepts of chemical metrology, the most complex is the calculation of uncertainties from different sources associated with a single measurement and incorporate them into the final result(s) as the expanded uncertainty(UE) with a defined level of reliability (e.g., at 95% CL). In this paper the concepts and practice of uncertainty calculation in analytical measurements are introduced by using the principles of statistics. The calculation procedure indentifies the primary sources of uncertainties and quantifies their respective contributions to the total uncertainty of the final results. The calculations are performed by using experimental data of Lead (Pb) analysis in soil by GF-AAS and pesticides analysis in wastewater by GC-MS method. The final result of the analytical measurement is expressed as: Result (mg/kg) = Measured Value of Analyte (mg/kg) ± Uncertainty (mg/kg), where the uncertainty is the parametric value associated with individual steps in measurements such as sample weighing(Um), extraction of analyte (Ue) (Pb from soil or pesticides from water), volumetry in measurement (Uv), concentration calibration(Ux), etc. The propagation of these individual uncertainties from different sources is expressed as combined relative uncertainty (Uc), which is calculated by using the formula:Combined uncertainty Uc/c = {(Ux/x)2+(Um/m)2+(Uv/v)2+(Ue/e)2+…}1/2The overall uncertainty associated with the final result of the analyte is expressed as Expanded Uncertainty (UE) at certain level of confidence (e.g. 95%). The Expanded Uncertainty is calculated by multiplication of Combined Uncertainty (Uc) with a coverage factor (K) according to the proposition of level of confidence. In general, the level of confidence for enormous data is considered at 95%, CL where K is 2. Hence, the final result of the analyte is expressed as: X ± UE (unit) at 95% CL, where UE = 2Uc.Journal of Bangladesh Academy of Sciences, Vol. 41, No. 2, 145-163, 2017