Adam Cooman

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Linearized active circuits:
transfer functions and stability

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2. Stability of linearized circuits under realistic assumption

We will work in the frequency domain to perform circuit analysis in this paper. We denote Laplace transforms with uppercase symbols, e.g. V=V(s)V=V(s) is a function of a complex variable ss which stands for the Laplace transform of the voltage v=v(t)v=v(t) which is a function of the time tt.

In the frequency domain, the behavior of a linearized component with nn ports is described by an admittance matrix [8][9] of size nn

[I1In]=[Y11Y1nYn1Ynn][V1Vn](2.1)\left[\begin{array}{c} I_{1}\\ \vdots\\ I_{n} \end{array}\right]=\left[\begin{array}{ccc} Y_{11} & \cdots & Y_{1n}\\ \vdots & \ddots & \vdots\\ Y_{n1} & \cdots & Y_{nn} \end{array}\right]\left[\begin{array}{c} V_{1}\\ \vdots\\ V_{n} \end{array}\right]\tag{2.1}

where the voltages V1VnV_{1}\ldots V_{n} are the node voltages with respect to the chosen reference node. The currents I1InI_{1}\ldots I_{n} are oriented so as to enter electronic components.

We adopt the paradigm that “what happens at very high frequencies is unimportant beyond passivity” and we set up a general definition of “realistic” that translates this requirement in mathematical terms for a large class of models.

Definition 1: A linearized multiport is said to be Y-realistic if its admittance matrix YY is meromorphic on C\mathbb{C} and there exists K>0K>0 such that for any ss satisfying (s)0\Re(s)\geq0 and s>K|s|>K: (i) Y ⁣(s)+Y ⁣(s)αIdY\!\left(s\right)+Y^{*}\!\left(s\right)\succeq\alpha\,\mathbf{Id} for some α>0\alpha>0. 1

Similarly a linearized multiport is said to be Z-realistic if its impedance matrix Z ⁣(s)Z\!\left(s\right) is meromorphic on C\mathbb{C} and there exists K>0K'>0 such that for any ss satisfying (s)0\Re(s)\geq 0 and s>K|s|>K':

(ii) Z ⁣(s)+Z ⁣(s)βIdZ\!\left(s\right)+Z^{*}\!\left(s\right)\succeq\beta\,\mathbf{Id} for some β>0\beta>0

A multiport is said to be realistic if it is Z and Y-realistic.

In Section 3 we provide examples of realistic component models and show how to verify whether a given model satisfies the definition. In the remainder of this section we prove some important properties of the partial transfer functions G(s)G\left(s\right) of circuits which comprise only realistic components. To that end, we compute the effect of IinI_{in} on the potential VkV_{k} using nodal analysis [9]. We use nodal analysis because we are exciting the linearised circuit with a small-signal current source. When a small-signal voltage source is used to determine the partial transfer function of the circuit, a mesh analysis can be used to obtain similar results [9]. When a combination of a voltage and current excitation is used, a modified nodal analysis or tableau method will be required to determine the partial transfer function, but this is outside of the scope of this paper.

To perform a nodal analysis we assign to each junction node jj a potential VjV_{j}, and to each edge kk an electric current IkI_{k}. One of the junction nodes, say VnV_{n}, is the ground (its potential is 00 by convention). We always assume that the graph associated to the circuit is connected. Specifically, we denote by V=(V1,,Vn1)t\mathbf{V}=(V_{1},\,\dots,\,V_{n-1})^{t} the vector of all node voltages (except VnV_{n}, the reference ground voltage) and by I=(I1,,Ip)t\mathbf{I}=(I_{1},\,\dots,\,I_{p})^{t} the vector of all currents in the branches. The (node-branch) incidence matrix of the circuit, say A=(Aij)\mathbf{A}=(A_{ij}), has n1n-1 rows corresponding to the nodes (except the ground) and pp columns corresponding the branches. It is defined by the rule:

{Aij=1 if edge ej is incident away from node i,Aij=1 if edge ej is incident towards node i,Aij=0 otherwise.\left\{ \begin{array}{l} A_{ij}=1\text{ if edge }%i=\text{tail}(j),\text{ or, equivalently, if } e_{j}\text{ is incident away from node }i,\\ A_{ij}=-1\text{ if edge }%i=\text{head}(j),\text{ or, equivalently, if } e_{j}\text{ is incident towards node }i,\\ A_{ij}=0\text{ otherwise.} \end{array}\right.

Since the graph is connected, AA has full row rank (n1n-1) [10]. Kirchhoff’s law gives us

AI=0(2.2)\mathbf{A}\,\mathbf{I}=\mathbf{0}\tag{2.2}

Next, we substitute currents with voltages using relations (2.1). For this, we form the branch admittance matrix, a block diagonal matrix

Yb=diag(Y1,Y2,,Yh)(2.3)\mathbf{Y}_{b}={\rm diag}(Y_{1},Y_{2},\ldots,Y_{h}) \tag{2.3}

where the YjY_{j} are the admittance matrices of the components in the circuit. With a convenient ordering of nodes and edges, it holds that I=YbAtV\mathbf{I}=\mathbf{Y}_{b}\mathbf{A}^{t}\,\mathbf{V}, and (2.2) yields

YV=(0Iin0)t,(2.4)\mathbf{Y}\,\mathbf{V}=\left(\begin{array}{ccccc} 0 & \ldots & I_{{\rm in}} & \ldots & 0\end{array}\right)^{t},\tag{2.4}

where IinI_{{\rm in}} is in position kk and Y(s)\mathbf{Y}(s) is a (n1)×(n1)(n-1)\times(n-1) matrix, called nodal admittance matrix, which is related to the branch admittance matrix through ([9] eq. (2.9.8))

Y=AYbAt.(2.5)\mathbf{Y}=\mathbf{A}\mathbf{Y}_{b}\mathbf{A}^{t}.\tag{2.5}

The presence of ideal active components like diodes and transistors may result in Y\mathbf{Y} being singular [7][11] at all frequencies. Suppose that the YkY_k are meromorphic functions on C\mathbb{C}. As meromorphic functions form a field [12], the determinant of a matrix with meromorphic entries is again a meromorphic function. We call therefore invertible as a meromorphic matrix, any meromorphic square matrix with non zero meromorphic determinant. If the YksY_k's are supposed to be Y-realistic we will show in next proposition that Y\mathbf{Y} in Equation (2.4) is invertible as a meromorphic matrix. By Cramer’s rule, we then have:

Vj=(1)k+jYj,kdetYIin.(2.6)V_{j}=(-1)^{k+j}\,\frac{\mathbf{Y}_{j,k}}{\det\mathbf{Y}}\,I_{{\rm in}}. \tag{2.6}

where Yj,k\mathbf{Y}_{j,k} denotes the minor of Y\mathbf{Y} obtained by deleting row jj and column kk. Thus, the voltage at node~jj depends linearly on IinI_{\text{in}} injected at node kk, and the ratio Vj/IinV_{j}/I_{in} is the partial transfer function or partial frequency response of the circuit at node jj from node kk.

Footnotes

  1. ABA\succeq B is in matrix sense, which means that ABA-B is positive definite.

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