### Overview of the Paper Structure  
This subsection presents this paper's roadmap. Section 2 systematically develops the all-analytic statistical model, starting from simple device statistical physics assumptions. The taken approach is to postulate the mismatch statistical model of Pelgrom's groundbreaking paper [5], retaining the lead term of the mismatch expansion therein, simply modeling the relative mismatch (relative STD (RSTD)) of the MOS transistor current source level as inversely proportional to the square root of the device active area. From Pelgrom's RSTD formula, we infer that the variances of the current sources must be proportional to their mean currents. Everything at the subsystem level then stems from this simple device-level posit on the variances of uncorrelated source currents. We proceed to model the electrical switching matrix of the eDAC by means of a matrix-based linear model accounting for the formation of the eDAC output current constellation, modeling the linear eDAC just using no more than elementary random signals theory, generalizing a matrix approach by Crippa et al. [7], as well as on our own matrix-based earlier formulation [13] (originally developed for oDACs but analogously applicable to eDACs). We derive closed-form formulas for the second-order statistics of the mismatch perturbations of the eDAC output current levels, as well as corresponding statistics for the constellation \"steps\", defined as the separations between neighboring levels. A novel feature of our linear-algebraic models of the eDAC is the formal derivation and explorations of the (skew-)centrosymmetry of the DAC random variates and statistical metrics, as taken up in Section 2.6. Figure 3: Monte Carlo simulations of the distributions of the source currents and output constellation currents for 3-bit (PAMS) UNIP eDACs, both ThWgt and BiWgt, overlayed with the corresponding analytic PDFs. (**a**): ThWgt UNIP eDAC: Distributions of the 7 independent random source currents, labeled \(I_{0}\), \(I_{1}\),..., \(I_{6}\). (**b**): ThWgt UNIP eDAC: Distributions of the corresponding output currents \(J_{1}\), \(J_{2}\),..., \(J_{8}\), of the output constellation, obtained by accumulating the 7 IID ThWgt source currents of part (**a**). (**c**): BiWgt UNIP eDAC: Distributions of the 3 independent random source currents, labeled \(I_{2}\), \(I_{1}\), \(I_{0}\), synthesized from the ThWgt source current array of seven 1500-vectors displayed in Figure 3a, based on the ”BiWgt structuring of an ThWgt current sources array” construction introduced in (249) of Section 2.8. (**d**): UNIP BiWgt eDAC: Distributions of the corresponding output currents obtained by passing the 3 IID BiWgt source currents of (**c**) via the UNIP \(8\times 3\) code matrix modeling the switching-in \(\mid\) out of 0, 1, 2 or 3 of the three sources and the summation of their currents. In Section 3, we relate the second-order statistical edifice constructed in Section 2, to the conventional definitions of the INL and DNL--the most prevalent DAC specifications. A key observation is that the INL\(\,\)! DNL eDAC metrics are inappropriate for statistical modeling in communication applications. To address this deficiency, we introduce simpler yet more suitable variants of INL\(\,\)! DNL, referred to as IEV\(\,\)! DEV. These proposed eDAC metrics are directly relevant to transmission link modeling. The IEV describes the deviations of the actual constellation from a nominal (nominal) constellation (with its end-points taken subtly different than those of the reference constellation in the INL definition). The DEV quantifies the \"eye-opening\" between adjacent traces in an eye diagram. We present simple, yet elegant derivations of the second-order statistics of the IEV\(\,\)! DEV random vectors, in the wake of mismatch errors of the array of eDAC current sources, based on our matrix-based approach. The mismatch-induced statistics of our new IEV\(\,\)! DEV metrics are much simpler to model statistically than those of the original INL\(\,\)! DNL.