Recent measurements using acoustic gas thermometry have determined the value of the Boltzmann constant, k , with a relative uncertainty less than 1 × 10 ⁻⁶ . These results have been supported by a measurement with a relative uncertainty of 1.9 × 10 ⁻⁶ made with dielectric-constant gas thermometry. Together, the measurements meet the requirements of the International Committee for Weights and Measures and enable them to proceed with the redefinition of the kelvin in 2018. In further support, we provide a new determination of k using a purely electronic approach, Johnson noise thermometry, in which the thermal noise power generated by a sensing resistor immersed in a triple-point-of-water cell is compared to the noise power of a quantum-accurate pseudo-random noise waveform of nominally equal noise power. The experimental setup differs from that of the 2015 determination in several respects: a 100 Ω resistor is used as the thermal noise source, identical thin coaxial cables made of solid beryllium–copper conductors and foam dielectrics are used to connect the thermal and quantum-accurate noise sources to the correlator so as to minimize the temperature and frequency sensitivity of the impedances in the connecting leads, and no trimming capacitors or inductors are inserted into the connecting leads. The combination of reduced uncertainty due to spectral mismatches in the connecting leads and reduced statistical uncertainty due to a longer integration period of 100 d results in an improved determination of k = 1.380 649 7(37) × 10 ⁻²³ J K ⁻¹ with a relative standard uncertainty of 2.7 × 10 ⁻⁶ and a relative offset of 0.89 × 10 ⁻⁶ from the CODATA 2014 recommended value. The most significant terms in the uncertainty budget, the statistical uncertainty and the spectral-mismatch uncertainty, are uncorrelated with the corresponding uncertainties in the 2015 measurements.

A value for the Boltzmann constant was measured electronically using an improved version of the Johnson Noise Thermometry (JNT) system at the National Institute of Standards and Technology (NIST), USA. This system is different from prior ones, including those from the 2011 determination at NIST and both 2015 and 2017 determinations at the National Institute of Metrology (NIM), China. As in all three previous determinations, the main contribution to the combined uncertainty is the statistical uncertainty in the noise measurement, which is mitigated by accumulating and integrating many weeks of cross-correlated measured data. The second major uncertainty contribution also still results from variations in the frequency response of the ratio of the measured spectral noise of the two noise sources, the sense resistor at the triple-point of water and the superconducting quantum voltage noise source. In this paper, we briefly describe the major differences between our JNT system and previous systems, in particular the input circuit and approach we used to match the frequency responses of the two noise sources. After analyzing and integrating 50 d of accumulated data, we determined a value: J K⁻¹ with a relative standard uncertainty of and relative offset from the CODATA 2014 recommended value.

We present a near-field scanning microwave microscope (NSMM) that has been configured for imaging photovoltaic samples. Our system incorporates a Pt-Ir tip inserted into an open-ended coaxial cable to form a weakly coupled resonator, allowing the microwave reflection S₁₁ signal to be measured across a sample over a frequency range of 1 GHz – 5 GHz. A phase-tuning circuit increased impedance-measurement sensitivity by allowing for tuning of the S₁₁ minimum down to −78 dBm. A bias-T and preamplifier enabled simultaneous, non-contact measurement of the DC tip-sample current, and a tuning fork feedback system provided simultaneous topographic data. Light-free tuning fork feedback provided characterization of photovoltaic samples both in the dark and under illumination at 405 nm. NSMM measurements were obtained on an inhomogeneous, third-generation Cu(In,Ga)Se₂ (CIGS) sample. The S₁₁ and DC current features were found to spatially broaden around grain boundaries with the sample under illumination. The broadening is attributed to optically generated charge that becomes trapped and changes the local depletion of the grain boundaries, thereby modifying the local capacitance. Imaging provided by the NSMM offers a new RF methodology to resolve and characterize nanoscale electrical features in photovoltaic materials and devices.

In preparation for the redefinition of the International System of Units (SI), five different electronic measurements of the Boltzmann constant have been performed using different Johnson noise thermometry (JNT) systems over the past seven years. In this paper, we describe in detail the JNT system and uncertainty components associated with the most recent National Institute of Standards and Technology (NIST) determination of the Boltzmann constant: k = 1.380642 9(69) × 10−23 J/K, with a relative standard uncertainty of 5.0 × 10−6 and relative offset of −4.05 × 10−6 from the Committee on Data for Science and Technology (CODATA) 2014 recommended value. We discuss the input circuits and the approach we used to match the frequency response of two noise sources. We present new measurements of the correlated noise of the 4 K on-chip resistors in the quantum-accurate, pseudorandom, voltage-noise source, which we used to estimate the correlated, frequency-dependent, nonthermal noise in our system. Finally, we contrast our system with those used in other measurements and speculate on future improvements.

In the electronic measurement of the Boltzmann constant based on Johnson noise thermometry, the ratio of the power spectral densities of thermal noise across a resistor at the triple point of water, and pseudo-random noise synthetically generated by a quantum-accurate voltage-noise source is constant to within 1 part in a billion for frequencies up to 1 GHz. Given knowledge of this ratio, and the values of other parameters that are known or measured, one can determine the Boltzmann constant. Due, in part, to mismatch between transmission lines, the experimental ratio spectrum varies with frequency. We model this spectrum as an even polynomial function of frequency where the constant term in the polynomial determines the Boltzmann constant. When determining this constant (offset) from experimental data, the assumed complexity of the ratio spectrum model and the maximum frequency analyzed (fitting bandwidth) dramatically affects results. Here, we select the complexity of the model by cross-validation-a data-driven statistical learning method. For each of many fitting bandwidths, we determine the component of uncertainty of the offset term that accounts for random and systematic effects associated with imperfect knowledge of model complexity. We select the fitting bandwidth that minimizes this uncertainty. In the most recent measurement of the Boltzmann constant, results were determined, in part, by application of an earlier version of the method described here. Here, we extend the earlier analysis by considering a broader range of fitting bandwidths and quantify an additional component of uncertainty that accounts for imperfect performance of our fitting bandwidth selection method. For idealized simulated data with additive noise similar to experimental data, our method correctly selects the true complexity of the ratio spectrum model for all cases considered. A new analysis of data from the recent experiment yields evidence for a temporal trend in the offset parameters.

Johnson noise thermometry (JNT) is a purely electronic method of thermodynamic thermometry. In primary JNT, the temperature is inferred from a comparison of the Johnson noise voltage of a resistor at the unknown temperature with a pseudo-random noise synthesized by a quantum-based voltage-noise source (QVNS). The advantages of the method are that it relies entirely on electronic measurements, and it can be used over a wide range of temperatures due to the ability of the QVNS to generate programmable, scalable, and accurate reference signals. The disadvantages are the requirement of cryogenic operation of the QVNS, the need to match the frequency responses of the leads of the sense resistor and the QVNS, and long measurement times. This review collates advice on current best practice for a primary JNT based on the switched correlator and QVNS. The method achieves an uncertainty of about 1 mK near 300 K and is suited to operation between 4 K and 1000 K.