Evaluating this expression yields: ppm * sqrt(2*Peak_Value_of_Noise_to_Carrier_Ratio)Įdit 2: Plugging in actual numbers: 50 ppm, -100dBc Noise to Carrier: 0.707 ns. What I meant was the peak value at the origin, and then decreasing by w^2 around it. Temperature coefcient:'100 ppm/K Resolution:' ' '12 bit (0.09° for 360° measurement range) 1) Convert temperature to degrees Kelvin, since values are in ☏, and calculate the temperature coefcient (tempco) error. We may therefore be able to estimate the standard deviation!ĭeviation of Jitter ~= sqrt(Integrate(ppm^2 * Peak_Value / w^2)).Įdit 1: The integrand, as presented above, is wrong. When calculating the theoretical m/z, remember to include the charge carriers (e.g. Amount of chemicals to obtain in small beaker per group: EDTA 150 mL pH buffer 40 mL (keep under hood until you need it) 0. Lastly we know that the autocorrelation function is linked to the power spectral density by means of fourier transform. The m/z range for an m/z of interest plus/minus a specified error in ppm. Unknown Water Sample Calculation Goal: ppm CaCO 3 mg CaCO 3 L H 2 O sample. Next, we can safely assume that the jitter can be modelled by a Gaussian Stochastic Process, as it is essentially a contribution of a number of thermal noise sources associated with resistances, resistive channels of MOSFETs, shot noise e.t.c. An estimate formula of the Noise-to-Carrier Ratio depends inversely on the quality factor squared of your LC tank which again scales inversely with your ppm, thereby: Noise-to-Carrier Ratio ~ ppm^2. Jitter will manifest itself in the spectral domain as a 'skirt' around your desired frequency. Following the comment of IC_designer_Rimpelbekkie is the path you want to take.

How to calculate ppm error