Abstract
Knots are intricate structures that cannot be unambiguously distinguished with any single topological invariant. Momentum space knots, in particular, have been elusive due to their requisite finely tuned longranged hoppings. Even if constructed, probing their intricate linkages and topological "drumhead” surface states will be challenging due to the high precision needed. In this work, we overcome these practical and technical challenges with RLC circuits, transcending existing theoretical constructions which necessarily break reciprocity, by pairing nodal knots with their mirror image partners in a fully reciprocal setting. Our nodal knot circuits can be characterized with impedance measurements that resolve their drumhead states and image their 3D nodal structure. Doing so allows for reconstruction of the Seifert surface and hence knot topological invariants like the Alexander polynomial. We illustrate our approach with largescale simulations of various nodal knots and an experiment which maps out the topological drumhead region of a Hopflink.
Introduction
In the pursuit of ever more exotic topological states, contemporary research has witnessed a shift from established topological insulator platforms with \({\mathbb{Z}}\) or \({{\mathbb{Z}}}_{2}\) topology to photonic, mechanical, and acoustic metamaterials^{1,2,3} that mimic topological nodal semimetals^{4,5,6,7,8,9,10}. The conceptual transfer from conventional electronic materials to such artificial structures allows for unprecedented control over individual couplings, and further permits access to any spectral regime of the band structure without limitations, as, e.g., implied by the chemical potential for electronic matter. The recent introduction of electric circuits for topological engineering^{11,12,13,14,15,16,17} brought about even greater accessibility and fine tuning, as well as much reduced cost. Most importantly, however, circuit connections transcend locality and dimensionality constraints, putting the implementation of couplings between distant sites of a highdimensional system and nearestneighbor connections on equally accessible footing. Furthermore, density of states divergences^{18} and even admittance bandstructure^{15,19} can be obtained with just impedance and voltage/current measurements, respectively.
Among topological structures, knots rank as among the most exotic, being intimately connected to ChernSimons theory which underlies the braiding of quasiparticles^{20,21}. In real space, knots are ubiquitous, being present in protein and polymer structures, optical vortices^{22} and, of course, everydaylife ropes. In momentum space, knotted configurations of band structure crossings (nodes) demonstrate their topological intricacies even more spectacularly, with their special “drumhead” surface modes generalizing the Fermi arcs of ordinary nodal semimetals.
To realize and image momentum space nodal knots in RLC circuits, two challenges have to be overcome. First, RLC circuits are reciprocal due to their components being symmetric from both ends, but mathematical models of nodal knots proposed thus far^{23,24,25,26,27} imply broken reciprocity. This apparent limitation has prevented nodal knot circuits from being developed so far, despite successes in nonknotted nodal loop circuits and metamaterials^{28,29,30,31}. Second, the momentum knots are subextensive 1D features of the 3D Brillouin zone (BZ), and great finesse is required in imaging them.
In this work, we show how these challenges can be overcome via (i) a special scheme for designing nodal knots circuits with mirrorimage partners, (ii) a new robust impedance measurement approach for imaging nodal knots and their accompanying drumhead surface states, and (iii) an instructive experimental demonstration of how the topological drumhead region of a nodal knot can imaged.
Results
Designer nodal knots from braids
The most natural route to realizing momentum space knots is via a 3D lattice with band intersections (nodes) along particular knotted trajectories. A generic reciprocal lattice with band intersections minimally contains two sites per unit cell, and can be written as a reciprocal (momentum) space graph Laplacian
where l_{0} is a uniform offset, f(k) is an even function of k, and τ_{x}, τ_{z} are the Pauli matrices. Nodes occur whenever its two eigenvalues (bands) \({l}_{0}\pm \sqrt{{[{\rm{Re}}f({\bf{k}})]}^{2}+{[\text{Im}f({\bf{k}})]}^{2}}=:{l}_{0}\pm  f({\bf{k}})\) coincide, i.e., yielding a vanishing gap 2∣f(k)∣ = 0. This is a complex constraint equivalent to the intersection of two level sets given by \({\rm{Re}}\ f({\bf{k}})=0\) and Im f(k) = 0, which hence traces out a 1D nodal line in the 3D BZ. Note that we have excluded τ_{y} terms, which will break the nodal line into isolated Weyl points. Generically, the locus of f(k) = 0 can correspond to broken arcs or arbitrarily intertwined closed loops. The topologically most interesting cases occur when a loop links nontrivially with itself, forming a nodal knot, or when multiple loops inseparably entangle to form a nodal link. In the following, we shall first show how f(k) can be constructed based on a desired knot or link structure, without restricting ourselves to any particular physical implementation. Subsequently, we show why its corresponding Laplacian J(k) can be most suitably implemented by an RLC circuit.
To design f(k), the first step is to unambiguously specify a desired knot or link. Intuitively, we can visualize a knot/link as a braid closure^{32}, i.e., as a collection of intertwining strands with their permuted ends joined together. (Fig. 1: The number of linked components is equal to the number of cycles in the decomposition of the permutation.) The precise sequence of the strand crossings identifies the knot/link, and is annotated as a braid word \({\sigma }_{1}^{\pm }{\sigma }_{2}^{\pm }...\), with σ_{i} indicating that the i^{th} string crosses above the (i+1)^{th} string from the left, and \({\sigma }_{i}^{1}\) if the crossing is from below. Two nonadjacent crossings commute: σ_{i}σ_{j} = σ_{j}σ_{i} for ∣i − j∣ ≥ 2; less obvious is the braid relation σ_{i}σ_{j}σ_{i} = σ_{j}σ_{i}σ_{j} which plays a fundamental role in the Yang–Baxter equation^{33}. Note that due to the braid relation, as well as Markovian moves that swap the closing strands^{34}, more than one braid word can correspond to a desired knot. Nevertheless, the specification of the braid uniquely identifies the knot. For instance, \({\sigma }_{1}^{2}\) gives the Hopflink, while \({\sigma }_{1}^{3}\) gives the Trefoil knot (Fig. 1).
The next step is to find an explicit form of f(k) that gives the knot/link corresponding to a desired braid. Mathematically, the knot/link exists as the kernel of the mapping \(f:{{\mathbb{T}}}^{3}\to {\mathbb{C}}\), which maps k in the 3D BZ \({{\mathbb{T}}}^{3}\) onto a complex number f(k). To make sure that f incorporates the information from the braid, we decompose it into a composition of mappings
i.e., \(f({\bf{k}})=\bar{f}(F({\bf{k}}))\) where F(k) = (z, w) maps k onto two complex numbers z(k) and w(k) in an auxiliary braiding space, which then yields f via the braiding map \(\bar{f}(z({\bf{k}}),w({\bf{k}}))=f({\bf{k}})\). To concretely understand this decomposition, we first note that a braid closure lives in the space \({\mathbb{C}}\times {S}^{1}\), since the position of N strands can be given by complex coordinates z_{1}(s), z_{2}(s), . . . , z_{N}(s), where s ∈ [0, 2π] is the periodic vertical “time” coordinate (Fig. 1a). Each braid operation corresponds to two halfrevolutions (windings) between two particles i.e. \({\sigma }_{i}^{\pm }\) corresponds to z_{i+1} − z_{i} → e^{±iπ}(z_{i+1} − z_{i}) with increasing s. We thus define \(\bar{f}(z,w)\) by analytical continuation to complex \(s=i\mathrm{log}\,w\) as
such that points satisfying the nodal constraint \(\bar{f}(z,w)=0\) lie exactly along the trajectories z_{j}(s). To use Eq. (3), one expresses each z_{j}(s) as a time Fourier series containing w = e^{is}, i.e., a polynomial in w, such that \(\bar{f}(z,w)\) becomes a Laurent polynomial of z and w. For instance, a Hopf braid can be parametrized by z_{1}(s) = −z_{2}(s) = e^{is} = w, which yields \(\bar{f}(z,w)=(zw)(z+w)={z}^{2}{w}^{2}\). This can be directly generalized to a braid of a (p, q) torus knot, which consists of p strands each of which twists for q revolutions before closure: \({z}_{j}(s)={e}^{\frac{i}{p}\left(2\pi j+qs\right)}\), yielding \(\bar{f}(z,w)={z}^{p}{w}^{q}\). Next, we need a criterion for suitable functions F(k) = (z(k), w(k)), that express z and w in terms of k. Ideally, F(k) should be able to “curl up” the braiding space \({\mathbb{C}}\times {S}^{1}\) into a solid torus in the 3D BZ, such that knots given by braid closures are faithfully mapped into nodal knots in the 3D BZ^{35} (Fig. 1). How this “curling” is accomplished is quantified by the winding number
where μ, ν, ρ, γ ∈ {1, 2, 3, 4} and z(k) = N_{1}(k) + iN_{2}(k), w(k) = N_{3}(k) + iN_{4}(k). It measures how many times the braid winds around the BZ. Generically, one will choose an F(k) with winding n = ± 1 to guarantee a onetoone mapping from a specific braid closure to a nodal knot in the BZ. An important caveat, however, is that n = ± 1 is not possible for a passive RLC circuit implementation due to its reciprocal nature. In the discussion surrounding Eq. (7) later, we shall explain how this seeming obstacle can be avoided systematically.
Our approach outlined so far generalizes existing approaches in the literature: In the approach of Ezawa^{23}, F(k) was chosen to be certain generalized Hopf fibrations, but there was no freedom of choosing f(z, w) for more general knot constructions; f(z, w) was further explored in ref. ^{36} in real space, but not in a toroidal momentum BZ where a nodal bandstructure can be found.
Characterizing nodal knot topology
A key feature of nodal knots is their interesting topological structure. Knotted lines of singularities in momentum space can be viewed as generalizations of Weyl points. In place of isolated sources of topological (Berry) flux, there are intertwined loops of “branch cuts”. While signatures of non trivial knot topology can manifest as optical nonlinearity enhancements in electronic nodal materials^{37,38}, we shall see that circuit implementations allow the nodal knots themselves to be directly reconstructed.
To mathematically characterize different knots, we first introduce the knot group. The knot group of a given knot K is the fundamental group \({\pi }_{1}({{\mathbb{T}}}^{3}\setminus K)\) of its complement in its ambient space, which in our context is the 3torus BZ \({{\mathbb{T}}}^{3}\). Physically, the complement \({{\mathbb{T}}}^{3}\setminus K\) is the part of the BZ containing nondegenerate eigenmodes, and the knot group indexes the space of nontrivial closed paths within this phase space. In the simple case of a nodal ring (unknot), \({\pi }_{1}({{\mathbb{T}}}^{3}\setminus K)\) consists of equivalence classes of trajectories characterized by their winding number around the ring, and is thus given by integervalued Berry phase windings \({\mathbb{Z}}\). In more complicated knots, there can be several inequivalent sets of windings, corresponding to different unique homotopy generators of \({{\mathbb{T}}}^{3}\setminus K\). For instance, the knot group of a (p, q) torus knot is given by 〈x, y∣x^{p} = y^{q}〉, since a path that winds p times around the “equator” can be deformed into one that winds q times around the “pole”. In the special case of the trefoil knot with (p, q) = (2, 3), the knot group 〈x, y∣x^{2} = y^{3}〉 is also isomorphic to the braid group with three strands: σ_{1}σ_{2}σ_{1} = σ_{2}σ_{1}σ_{2}, as evident from identifying x = σ_{1}σ_{2}σ_{1} and y = σ_{1}σ_{2}. Yet, in general, the presentation for the knot group can take diverse reparametrized forms (i.e. 〈x, y∣xyx^{−1}yx = yxy^{−1}xy〉 for the figure8 knot), and is hence by itself insufficient for topological classification.
In order to faithfully distinguish topologically inequivalent knots, various knot invariants have been developed. Simple invariants such as the linking number or knot signature can be easily computed by examining the crossings, but only have limited discriminatory power. A more sophisticated approach involves the Chern Simons path integral^{20}, which encapsulates topological information on the nodal singularities through certain knot polynomials, i.e., Jones polynomials, depending on the chosen gauge group. In our physical setup with classical circuits, another wellestablished invariant known as the Alexander polynomial will be most experimentally accessible. Starting from the topological surface “Drumhead” modes, one can reconstruct the Seifert surface, which is an orientable surface in the 3D BZ whose boundary is the nodal knot/link, and compute the Alexander polynomial from its homology properties.
Surface states of knots
Since nodal knots/links consist of closed loops, they form the boundary of topological surface drumhead modes in the projected 2D surface BZ. Intuitively, drumhead modes can be construed as Fermi arcs traced out by Weyl points moving along the nodal lines. If a nodal structure were to be deformed across a topological transition, i.e., till the loops of a Hopf link intersect, the shape of the drumhead regions along suitable projections must also transition discontinuously i.e. from two overlapping regions to two disjoint regions. For each possible surface termination, the drumhead regions form the surface projections (shadow) of a tight, i.e., minimal area Seifert surface (Fig. 2). In this sense, the drumhead modes on differently oriented boundary surfaces are just different “holographic” projections of the same tight Seifert surface living in the 3D BZ. Note that a Seifert surface is itself not a topological invariant, since it is not unique: for instance, \({\rm{Re}}[f({\bf{k}})]> 0\), \({\rm{Re}}[f({\bf{k}})]<0\), Im[f(k)] >0 and Im[f(k)] <0 are all valid Seifert surfaces, albeit not all tight.
To construct a topological invariant such as the Alexander polynomial, we hence need information on how the Seifert surface links with itself: we consider the linking of its 1sthomology loops α_{1}, α_{2}, . . . , α_{l} with \({\alpha }_{1}^{\prime},{\alpha }_{2}^{\prime},...,{\alpha }_{l}^{\prime}\) of a lifted Seifert surface defined from a infinitesimally shifted Laplacian \(L^{\prime} ({\bf{k}})=L({\bf{k}})\epsilon {\tau }_{j}\), with j = x or z. This shift creates a parallel Seifert surface infinitesimally displaced in a way consistent with the knot orientation given by the vector \({\nabla }_{{\bf{k}}}{\rm{Re}}\ f({\bf{k}})\times {\nabla }_{{\bf{k}}}{\rm{I}}m\ f({\bf{k}})\). The l × l Seifert matrix S_{ij}, which captures the twisting structure of the Seifert surface, is then given by the linking number of α_{i} and \({\alpha }_{j}^{\prime}\), with l being the number of homology generators^{34,39}. From that, one can obtain the Alexander polynomial invariant as
For instance, as further elaborated on in the methods section, A(t) = t + t^{−1} − 1 for the trefoil knot. General heuristics for constructing and visualizing the Seifert surface for a given nodal bandstructure are outlined in Fig. 2d.
Constructing and measuring knots in circuits
Having detailed their mathematical construction and characterization, we now describe how nodal knots can be concretely implemented and detected in electrical RLC circuits via both simulations and experiments. An RLC circuit with N nodes can be represented by an undirected network with graph nodes (junctions) α = 1, . . . , N connected by resistors, inductors and capacitors. Its behavior is fully characterized by Kirchhoff’s law at each junction, which takes the matrix form
where I_{α} is the external current entering junction α and V_{β} is the potential at junction β. Physically, each entry J_{αβ} of the Laplacian J physically represents admittance (AC conductance): in the submatrix spanned by junctions (α, β), an element with impedance r_{ab} contributes \({r}_{ab}^{1}\left(\begin{array}{cc}1&1\\ 1&1\end{array}\right)\) to the Laplacian, where r_{ab} = R, iωL and (iωC)^{−1} for the RLC components, respectively. The strictly reciprocal (symmetric) nature of these components constrains the possible forms of the Laplacian. In particular, for a circuit array with two sites per unit cell, \({\rm{Re}}\ f({\bf{k}})\) and Im f(k) in the Laplacian of Eq. (1) must be even^{40} in powers of k. This constraint severely restricts the prospects of faithfully “curling” a braid into a 3D BZ, such that each desired braid crossing is mapped onetoone onto the resultant nodal structure. This is because nodal knots necessarily contain unpaired 2D Chern phase slices, which require reciprocity breaking. Mathematically, it corresponds to the impossibility of achieving an F(k) winding of ∣n∣ = 1 (Eq. (4)) without sine terms. Primarily for this reason, nodal knots have not appeared in existing linearized reciprocial circuit architectures, or related settings of classical topological matter.
In this work, our key insight is to instead realize pairs of nodal knots related by mirror symmetry, such that reciprocity does not have to be broken. This can be achieved via a mapping F(k) = (z(k), w(k)) such as
which possesses opposite windings of n ≈ ±1 in each of the two halves of the 3D BZ given by k_{z} > 0 and k_{z} < 0 (Fig. 1b). Provided that w is raised only to even powers in \(\bar{f}(z,w)\), the Laplacian will be even in k, and hence realizable in an RLC, and as such reciprocal, circuit.
The overwhelming advantage of topolectrical circuit array implementations is that nodal structures naturally manifest as robust impedance peaks, i.e., electrical resonances. Consider a multiterminal measurement with input currents and potentials given by the I_{α} and V_{β} components respectively (c.f. Eq. (6)). In general, the impedance Z_{ab} between modes a and b is given by
where j_{λ} and ψ_{λ} are the corresponding eigenvalues and eigenvectors of the circuit Laplacian J. Note that the modes a, b are not necessarily the realspace nodes α, β appearing in Eq. (6); in the translationinvariant circuits that we consider, they can also refer to quasimomentum modes from the Fourier decomposition of multiterminal measurements. Importantly, for circuits designed such that j_{λ} ≈ 0 along the nodal loops/knots or their drumhead regions, Z_{ab} should signal pronounced divergences (resonances) when either a or b coincide with the nodal regions. More generally, Z_{ab} should diverge strongly whenever the Laplacian exhibits a zeroeigenvalue flat band with divergent density of states, since j_{λ} ≈ 0 for extensively many λ, unless ψ_{λ}(a) = ψ_{λ}(b) at terminal a, b.
For the sake of concreteness, we specialize to a periodic circuit network with a repeated unit cell structure. This allows us to rewrite Eq. (6) as
with x, y labeling the unit cell positions in the circuit, while i, j = {1, 2} labels the two sublattice nodes inside each unit cell. By exploiting the translational invariance of unit cells in the circuit, J_{(x, i),(y, j)} = J_{i,j}(x − y), we can find the irreducible representations of the translational group of J by a Fourier transformation in the real space coordinates
In Eq. (10), we sum over all unit cell positions r in the circuit network. We define the Fourier transformation of J to be in the directions perpendicular to the open boundary surface. The dimension of the resulting matrix J(k) is fixed by the number of circuit nodes that do not transform into each other by translation. By diagonalizing J(k), we find the admittance band structure \({j}_{n}({\bf{k}}),n\in \{1,\ldots ,\dim (J({\bf{k}}))\}\) of the circuit network as a mapping of quasimomentum k to admittance eigenvalues of J. The fully periodic circuit network is then constructed such that the admittance band eigenvalues are given by the absolute value of f, j_{±}(k) = ±∣f(k)∣. The kernel of the fully periodic admittance band structure features onedimensional closed nodal loops in its 3D BZ, that are induced by the corresponding mapping \({{\mathbb{T}}}^{3}\to {\mathbb{C}}\) inherited from the function f(k). In an experimental setting, it is possible to extract the admittance band structure by performing N linearly independent measurement steps, where N describes the number of inequivalent nodes in the network. Each step consists of a local excitation of the circuit network and a global measurement of the voltage response, from which all components of the Laplacian in reciprocal space can be extracted. Consequently, the admittance band structure is found by a diagonalization of J(k) for each k.
In the following, we show Xyce^{41} simulation results of the prescribed measurement procedure with periodic (Fig. 3) as well as open boundary conditions (Fig. 4) for circuits featuring a Hopflink, trefoil knot and figure8 knot. The experimental details for the Hopflink are described in the Methods section.
Before proceeding to more involved nodal knots, we illustrate our approach through the simplest example of a nontrivial linked nodal structure—the Hopflink (Fig. 1c). With f(k) = z(k)^{2} − w(k)^{2} (z_{1,2}(s) = ± e^{is} in Eq. (3)), it is the simplest possible nontrivial nodal structure, with at most nextnearest neighbor (NNN) unit cells connected by capacitors C, C/2, C/4 or inductors L, L/2, L/4 in each direction (see “Methods”). In steadystate Xyce AC simulations, where the frequency parameter is set by the external excitation, the impedance peaks at \({\omega }^{2}=\frac{1}{LC}\) indeed accurately delineate the two interlinked nodal rings, as shown in Fig. 3a. Its surface projections are even more accurately resolved as drumhead regions when the measurements are taken on open boundary surfaces normal to \(\hat{x}\) and \(\hat{y}\), as shown in Fig. 4a. No drumheads are expected for \(\hat{z}\) open boundary surfaces, since there is another mirrorimage nodal structure related by k_{z} → − k_{z}.
We next consider the trefoil knot, which is defined by f(k) = z(k)^{2} − w(k)^{3}. While it, even after topologypreserving realspace truncations (see “Methods”), still necessitates longerranged connections, circuit networks conveniently allow to accomodate for such couplings. In Figs. 3b and 4b, we present the simulation results of the detailed imaging of a nontrivially knotted nodal loop and its drumhead surface projections, which also showed remarkable agreement with theoretical expectations.
Our approach can also be conveniently applied to more obscure nontorus knots where f(z, w) is not a polynomial in z and w. For illustration, we simulate the circuit with a Figure8 knot nodal structure with \(f({\bf{k}})=64\ z{({\bf{k}})}^{3}12\ z({\bf{k}})(3+2(w{({\bf{k}})}^{2}{\bar{w}}{({\bf{k}})}^{2})) 14(w{({\bf{k}})}^{2}+\bar{w}{({\bf{k}})}^{2})(w{({\bf{k}})}^{4}\bar{w}{({\bf{k}})}^{4})\), where \(w({\bf{k}}),\bar{w}({\bf{k}})=\sin {k}_{x}\pm i\sin {k}_{y}\). The Figure8 knot belongs to the more general class of knots known as lemniscate knots, where the equivalent braid cannot be expressed the braiding of p strands with q revolutions, and requires the appearance of both w and \(\bar{w}\) in its f(k)^{35}. Despite its ostensibly more complicated appearance, its nodal structure and surface drumhead states, shown in Figs. 3c and 4c, respectively, can be easily obtained from impedance measurements.
Experimental mapping of surface drumhead states
A highlight of this work is the experimental verification of our design of momentumspace nodal structures. Due to the topological significance of surface drumhead states, as well as their extensively large density of states, our experiment shall involve the mapping of the drumhead state of the nodal Hopf Link shown in Fig. 4a, where k_{y} and k_{z} are synthetic coordinates. This surface was chosen due to the distinctive “doubleloped” structure of the drumhead state, which should prominently show up as a region of elevated topolectrical impedance.
The first step in experimental circuit design is to simplify the realspace lattice structure. After optimal truncation and tuning of the xdirection couplings (see “Methods”), we obtained a slightly modified Hopflink with qualitatively similar double lobes in its drumhead region (Fig. 5a). Note that unlike the topological drumhead modes themselves, the elevated region consists of extra “ridges and valleys” due to additional contributions from other bands in Eq. (8). This circuit is physically implemented with an array of connected printed circuit boards (PCBs), each representing one unit cell, which can be adjusted to accurately correspond to different (k_{y}, k_{z}) points by tuning the inductors (Fig. 6 of Methods). Enabled by individually addressing the nodes, our tuning approach allows each inductance to be reliably adjusted by −50% to +25% of its original manufactured value, realizing to our knowledge the most accurately tunable circuit in the literature of topolectrical circuits to this date. To realize the required variety of capacitance values, we have implemented each logical capacitor as an appropriate parallel configuration of a few commercially available capacitors (see “Methods”). All parametric tunings are relegated to the inductances, since variable inductors are more reliably tuned than variable capacitors in practice.
While the topological robustness of drumhead states increases with the number of unit cells N, so do the destabilizing contributions from parasitic resistances and components uncertainties. As simulated in Fig. 5b for realistic component values, we have found that a rather low N = 9 already gives rise to a robustly visible drumhead region of elevated impedance. Importantly, this robustness is well corroborated against the experimental impedance data presented in Fig. 5c. Even with only 14 (k_{y}, k_{z}) data points, each obtained through careful tuning, we have observed a very high fidelity between the expected and measured impedance values, as also visually evident from the almost perfect match of the blue/red (low/high imepdance) points between simulation and experiment (Fig. 7 of Methods). To mitigate the effects of parasitic resistance and component uncertainty, we have also taken advantage of a machine learning algorithm that choses (k_{y}, k_{z}) sampling points that remain the most impervious to these uncertainties (Fig. 8 of Methods).
Besides conclusively demonstrating the experimental viability of mapping out nodal drumhead states, our experiment also pushes the stateoftheart in tunable topolectrical circuits, where even minute uneveness between unit cells can potentially affect the circuit band structure significantly. As further elaborated in the “Methods” section, further refinement of this technique through microcontrollers can lead to even more accurate automated tuning that can eventually realize topological pumping in quasiperiodic (AubryAndreHarper) circuits.
Discussion
We have introduced an experimentally accessible approach for realizing generic momentum space nodal knots. Our proposed systems can be easily implemented in RLC circuit setups, whose nodal admittance band structure is directly characterizable via impedance measurements. A key theoretical novelty for accomplishing this is our choice of momentum space embedding functions z(k), w(k), which permits the knotting (and not just linking) of momentum space nodal structures without breaking reciprocity. This not only allows for easy implementation of almost any desired knot from its corresponding braid, but also for a robust surface drumhead state characterization of the knots. Combined with multiterminal impedance measurements in the bulk, our RLC nodal knot framework provides an unprecedentedly direct access to the Seifert surface structure and knot invariants. Our approach is explicitly demonstrated through largescale simulations of three different nodal knot circuits, as well as an experiment which maps out the drumhead surface state of a nodal Hopflink. It established the proof of principel how to realize any nodal knot in a topolectric circuit.
As the next refinement step of the analytic simulation of the electronic setup, one needs to take into account parasitic resistances, in particular those that derive from the inductors. Here, the dissipative, i.e., nonHermitian generalization of our idealized Hermitian circuit setup opens up yet another unexplored territory of topological matter^{42,43,44}, i.e., nonHermitian nodal knot systems^{40,45}. We defer this analysis to future work. In order to directly remedy the parasitic effect from the inductors, the most viable solution is to increase the AC frequency scale into the Megahertz regime at which the nodal knots are observed. This would also help with the higher spatial intergration or our nodal knot circuits. Setting up a new generation of Meghertz topolectric circuits will hence be a prioritized experimental future objective.
Methods
Circuit simulation details
This section elaborates on the setup of the circuits that we simulated. As detailed in the main text, the desired knot or link is given by the kernel of a knot function f(z, w) that maps the 3D BZ \({{\mathbb{T}}}^{3}\) to a complex number \({\mathbb{C}}\). The first step in determining the circuit design is the construction of the function f(z, w) from the corresponding braid through the procedure we had outlined. In the next step, we find suitable functions z(k) and w(k) that faithfully map the knot to the kernel of f(k). To be able to implement the corresponding function f(k) in a circuit environment i.e. a tightbinding lattice that preserves reciprocity, we implement two mirror images of the circuit in the BZ that are related by k_{z} → −k_{z}. The Laplacian for the circuit simulations is then set up as (note the slightly different definition of f from Eq. 1 of the main text)
The circuit connections are then designed such that they form the Laplacian J(k). This is achieved by expanding the real and imaginary part of f as single cosine terms and implementing the separated terms as internodal connections in the circuit. Those connections need to fulfill two criteria. First, they need to realize the proper real space linkage between two nodes to replicate the specified term in the (2 × 2) Fourier transformed Laplacian. Second, the magnitude of those elements is to scale with the prefactor of the corresponding cosine term. A positive value is implemented by a capacitor and a negative value by an inductor. Finally, we need to account for the total node conductance in the circuit setup by implementing adequate grounding terms. The scales of the capacitances and inductances are chosen to be C = 1 nF and L = 10 μH, yielding a resonance frequency of
f_{0} will be the operating frequency for all performed simulations, where signatures of the prescribed nodal knots or links emerge. At this specific frequency, the inductances defined act as negative capacitances due to their π relative phase shifts. For reasons of numerical stability, we include additional ground connections of C_{ground} = 100 nF and R_{ground} = 1 kΩ at every node in the circuit. These terms just enter as an identity matrix contribution \({l}_{0}{\mathbb{I}}\) and can be subtracted out after the band structure has been reconstructed from the simulation data. The Laplacian of the circuit is then shifted as \(J({\bf{k}})\to J({\bf{k}})+{l}_{0}{\mathbb{I}}\), and its two band admittance spectrum is given by
To recreate the admittance band structure, we use the measurement scheme initially described in^{19}. There and in all our simulations, each measurement step consists of a local excitation of the circuit at one node through an AC driving voltage via a shunt resistance and a global measurement of the total voltage profile at all nodes in the circuit. The shunt resistance enables the measurement of the input current that is fed into the circuit.
From the global response of the circuit, we can reconstruct the Fourier coefficients of J in reciprocal space and diagonalize J(k) for every k. This measurement procedure must be repeated M times, where M describes the number of nonequivalent nodes in the circuit network to be able to reconstruct the full Laplacian J(k). From the admittance band structure, we then distill the closed nodal loops of the specified model by selecting the imaginary admittance eigenvalues, that are smaller than a globally chosen upper threshold. This upper bound is selected such that the valley points corresponding to the zero nodal points on the knot or link are recovered, but no additional points appear in regions with small gradients close to the nodal line. Due to the discretization of the BZ, we recover only a discrete set of nodal points in the BZ. This drawback can be counterbalanced to some degree by simulating circuit networks with different dimensions in terms of unit cells. This way, we enhance our grid resolution in reciprocal space and obtain a more precise result due to an increased number of data points on the knot or link.
Similarly, the OBC simulations are evaluated by extracting admittance eigenvalues smaller than a chosen limit. Those points in the projected BZ form 2D areas, as shown in Supplementary Fig. 2. These 2D areas correspond to projections of the Seifert surface bounded by the corresponding link or knot onto the direction of the open boundary surface. The corresponding zeroadmittance eigenstates amount to the socalled Drumhead states that are exponentially localized at the boundary with an inverse localization lengths given by their imaginary gaps^{46,47}. With these preliminary explanations, the only remaining requisite to perform the individual simulations is the specification of the employed knot function f(z, w) and the functions z(k) and w(k). Note that since f(k) in general consists of an exponential tail of distant couplings in real space^{46,48,49}, some gappreserving real space truncation of its real and imaginary parts is necessary for actual implementations. For the most part, this presents no additional challenges, and can be adapted to conform to the specifications of available actual electronic components. We also need to define an upper admittance threshold for resonance to extract the nodal points from the obtained simulation data.
We perform Xyce simulation for different system sizes in order to increase the resolution of the knot in the BZ. Since the reciprocal space consists of discrete points of allowed quasimomenta for any finite number of unit cells, we cannot to trace out the knot exactly. In order to increase the density of samples, one can increase the system size, but this increases the computational costs. Our alternative approach is to create several copies of the same setup, but with varying system sizes. Choosing the number of unit cells as coprimes of one another increases the sampling density of the combined momentum grid without the need for creating a very large system.
In the Xyce simulations, we create spice netlists which represent a circuit network consisting of capacitors and inductors described by a Laplacian in the form of (11) and perform AC analyses on them. In order to simulate one measurement procedure step necessary to reconstruct the admittance band structure, we connect an ideal voltage source via a shunt resistor to the circuit. As the parameters of amplitude voltage and shunt resistance can be chosen arbitrarily in a simulation, we used 1 V and 1 Ω. The AC analysis frequency is given by f_{0}.
Drumhead state experiment
The objective of our experiment is to reconstruct the surface topological drumhead state of a simplest illustrative nodal structure, the Hopflink, as shown in Supplementary Fig. 2a. For that, the physical circuit must possess a Laplacian L^{C} that is proportional to the Laplacian L of a Hopflink at a particular resonant AC frequency ω_{0}. For a streamlined implementation, we deformed the Hopflink Laplacian from the Supplementary Information such that it contains up to only nearest neighbor (NN) connections along the surface normal \(\hat{x}\) while retaining a qualitatively similar nodal structure (Fig. 5). Explicitly, we require
where the components of the deformed Hopflink Laplacian are given by \({L}_{z}=4\cos {k}_{x}(2\cos {k}_{y}\cos {k}_{z})2(54\cos {k}_{y}+\cos 2{k}_{y}+\cos {k}_{y}{k}_{z}4\cos {k}_{z} \cos 4{k}_{z}+\cos {k}_{y}+{k}_{z})\)and \({L}_{x}=(1+2\cos 2{k}_{z})(\cos {k}_{z}+\cos {k}_{y}+\cos {k}_{x}2)\). One way to satisfy Eq. (14) is to design the physical circuit such that its corresponding components \({L}_{x}^{C},{L}_{z}^{C}\) are of the forms
where v, t, t_{AB}, g_{A} and g_{B} depend parametrically on k_{y}, k_{z} as follows:
() can be realized with an LC circuit array in the form of a 2leg ladder with N unit cells (rungs) and 2N nodes in total (Fig. 9). Each term x ∈ [t, v, − t, t_{AB}, g_{A}, g_{B}] is represented by a parallel configuration of a tunable inductor L_{x} and capacitor C_{x} of appropriate value, such that its admittance
is of the required (k_{y}, k_{z})dependent value t, v, −t, t_{AB}, g_{A} or g_{B} at a particular ω = ω_{0}. As elaborated later, it suffices to vary only the inductances to sweep through the entire range of (k_{y}, k_{z}) stipulated by the size of the drumhead region in Fig. 5. Here C_{0} is an arbitrarily defined reference capacitance value that offers a free rescaling degree of freedom in the tuning, and c_{x} is the corresponding dimensionless capacitance of element x. Each element proportional to \(2(1\cos {k}_{x})\) couples two neighboring unit cells, while each term in the offdiagonal \({L}_{x}^{C}\) couples the upper and lower rungs. Note that our proposed circuit requires only LC components i.e. inductors and capacitors, with positive and negative resistors truncated off without appreciably changing the shape of the drumhead region. That said, with the contact and parasitic resistances intrinsic to an experimental circuit, some of these resistances will be inevitably reintroduced. These, however, also lead to no significant modification of the drumhead region, as verified via a simulation with realistic amounts of parasitic resistances and component uncertainty (Fig. 7b).
Our circuit is built with interconnected PCBs, each representing one unit cell, as shown in Fig. 9c. With a strategic choice of C_{0} and frequency ω_{0}, it is possible to scan through the entire relevant range of k_{y}, k_{z} by just tuning the inductances alone. As elaborated later, this can be accurately achieved through the use of ferrite rods and shorted wire loops within/around each inductor. The required fixed capacitances are realized by parallel combining commercially available capacitances into logical capacitors. The specifications of these logical components, as well as that of their underlying physical capacitors, are detailed in Supplementary Tables 1 and 2.
A major consideration of topolectrical circuit design is that imperfections from parasitic/contact resistances and component uncertainties should not change the measured impedance and hence Laplacian band structure significantly. Inductors are commonly manufactured with +/−10% inductor value uncertainty, and a typical parasitic resistance that scales at a rate of 2.45 Ω/1 mH. In theory it is possible to decrease the impact of parasitic resistance by increasing the inductance, but this cannot be done in practice because larger inductors typically require longer wires which increases parasitic resistance. Capacitors on the other hand are commonly manufactured with +/−5% uncertainty, and have negligible parasitic resistance compared to PCB trace wires, which possess 0.024 Ω/cm. In the case of capacitors, the effect of parasitic resistance may be decreased by picking smaller capacitors. However, choosing smaller capacitors requires larger inductors for the same frequency used in impedance measurement, which increases parasitic resistance, or requires a higher frequency. Therefore ideally one chooses the ω_{0} to be as high as possible depending on their signal generator/impedance measurement equipment, and then chooses a value of C_{0} that results in the smallest effect of parasitic resistance in the capacitors, but not too small to increase the inductor values and inductor parasitic resistances. Such imperfections can be modeled as additional serial resistivities on inductances and capacitances that are rescaled by a factor of 1 + u, u a random variable, as illustrated in Fig. 9b for the measured impedance across the entire circuit (between nodes 1A and NB).
Impedance data measurement and analysis: To map out the drumhead state, we measured the impedance across the first and last nodes of the circuit (1A and NB) at a number of strategically determined (k_{y}, k_{z}) points that are relatively insensitive to component disorder, as elaborated in the following subsection. As presented in Fig. 5, the drumhead region is indeed clearly visible as a region of elevated impedance, in close agreement with the imperfectioncorrected simulation (Fig. 7b.). The simulation also revealed that parasitic resistance in general decreased the impedance contrast by reducing the high impedance in the drumhead region and raising the low impedance outside of it. Component uncertainty increases the variance in the measured impedance at each individual (k_{y}, k_{z}) point, such that a larger number of measured (k_{y}, k_{z}) points are needed to average over the noise.
While a very large N will yield the most topologically robust drumhead state in an ideal setting, in practice that will also introduce much larger accumulated parasitic resistances and total component uncertainties, not to mention the copious resources needed. As such, we have built our circuit with N = 9 cells as a compromise between topological localization, noise and cost. The complete setup is pictured in Fig. 10. After tuning all inductors in accordance to the k_{y}, k_{z} values, the impedance across the entire circuit is measured by attaching nodes 1A and NB to a voltage divider and observing the voltage drop across the circuit using an oscilloscope. After correcting for possible frequency shifts due to uncertainties in the tuning circuitry (elaborated later), we indeed measured a distinctive cluster of elevated impedances in the drumhead region, as shown in Fig. 5c and analyzed in Fig. 7.
Even though the experimental setup also suffers from imperfect tuning of inductor values and additional parasitic resistances from the solders linking the repeating PCBs (unit cells), we are still able to reliably distinguish the low/high impedance points and hence delineate the correct drumhead region. Experimentally, we were able to measure 5 points in the lowlying regions \({Z}_{\exp .}<160\Omega\), 6 points in the elevated region \({Z}_{\exp .}> 250\Omega\) and 3 points in the borderline region between them. The 5 points in the lowlying region correspond to the “inside” of the elevated region k_{y} < 0.5, 0.6 < k_{z} < 1.0, and the region to the left of the elevated region k_{z} < 0.4. The square root of the average normalized error of each measured point was 0.2, and the coefficient of correlation between simulated and measured data were 0.743. When excluding three points (ky,kz) = (0.9, 1.28), (0.96, 1.07), (0.07, 1.26), which according to simulation are in regions with very high local variance within a small k radius around those points (see Fig. 7b, c, and Supplementary Tables 4, 5, and 6), the coefficient of correlation increases to 0.863. These relatively unstable points were chosen for measurement in order to map a complete ring around the drumhead, but are difficult to measure due to the extreme variance in the region k_{z} > 1.0. With larger circuits with much higher N, for example N = 30, the region k_{z} > 1.0 becomes easier to measure due to reduced variance at higher N (see Fig. 5a.).
Machine learning assisted selection of sampling points: To minimize the effect of uncertainties and reduce the number of (k_{y}, k_{z}) points needed to reconstruct a prominent drumhead region of elevated impedance, we used a NearestNeighbor machine learning algorithm to select (k_{y}, k_{z}) sampling points which are optimally impervious to capacitor and inductor uncertainties, see Fig. 8. This is important for reducing experimental costs, as well as reducing the impact of inevitable component uncertainties.
We associate each sampling point with Z_{avg}, which is the impedance of a particular k_{y}, k_{z} point averaged over a large number of randomly generated component uncertainties within the tolerance range, +/−1% for both inductors and capacitors, and a fixed parasitic resistance of R_{pL }= 0.11 Ω, R_{pC} = 0.03 Ω. Z_{SD} is the corresponding standard deviation associated with many samples of a particular k_{y}, k_{z} point simulated with component uncertainty and parasitic resistance, and Z_{ideal} is the predicted impedance without component uncertainty or parasitic resistance. To ensure the integrity of the measured data, we will first desire that ∣Z_{avg} − Z_{ideal}∣/∣Z_{ideal}∣ is small. This is not necessarily the case when the impedance depends highly nonlinearly with the capacitances and inductances. Furthermore, Z_{SD}/Z_{ideal} should be minimized too, so as to mitigate the variance caused by the uncertainties.
Given an initial set of sampling points, our selection algorithm improves on them according to the aforementioned metrics, and outputs a more desirable set of points. As elaborated in Fig. 8, the NearestNeighbor unsupervised learning algorithm efficiently determines a smaller allowed search space, allowing the filtering of desirable measurement points to be performed with much less computational resources compared to a brute force approach. We have optimized the selection of sampling points only within the drumhead region, since high impedance points are more sensitive to parasitic resistance and component uncertainty.
Tuning of each unit cell through variable inductors: To realize the Laplacian at a specific (k_{y}, k_{z}) point, the admittance of each coupling unit, i.e., logical component x must be tuned to correspond to G_{x} in (18). This may be done by attaching each coupling unit to a voltage divider and AC power supply, and observing the voltage drop across the coupling unit using an oscilloscope, see Fig. 10. The coupling unit is placed in series with a calibrating resistance R_{t} = 1.5 kΩ, a V_{supply} = 5 Vpp voltage is supplied across the entire circuit, and the voltage amplitude V_{x} between the ends of the coupling unit is measured. The inductance of the variable inductor in each coupling unit shall be tuned until V_{x} matches with
Each variable inductor is tuned using ferrite rods or shorted wire loops placed close to the fixedvalue inductors, see Fig. 6. To increase the inductance, a ferrite rod is placed closer to the inductor to better align the internal magnetic fields in it. Conversely, to decrease the inductance, a wire loop is used to “shield” the inductor from any change in magnetic field, thereby decreasing its selfinductance. The wire loop decreases the inductance of the original fixedvalue inductor due to an opposing induced current, as derived below. First, the e.m.f induced in the wire loop is equal to
where L_{m} is the mutual inductance between the fixedvalue inductor and the wire loop, and i(t) is the current running through the fixedvalue inductor. The current in the wire loop is then
where L_{wl} is the selfinductance of the wire loop, and R_{wl} is the resistance of the wire loop. At sufficiently high AC frequencies, we may ignore the resistance in the wire loop, such that the current induced in the wireloop is simply
The total flux on the original fixedvalue inductor is then
implying a decreased inductance in the original fixedvalue inductor:
Using a combination of the ferrite rod and wire cage, we were able to alter the inductance of a fixedvalue inductor component by −50% to +25% of its original manufactured value.
With this range of variable inductances and the known stipulated values of the logical components given in Supplementary Table 1, we selected default fixed inductor values of 39 uH for the t, v coupling units, and 10 uH for the remaining −t, t_{AB}, g_{A}, g_{B} units. The default capacitor values were selected to reproduce the reference point (k_{y}, k_{z}) = (1.02, 0.75) via ((18)) without any alteration of the fixedvalue inductors. Because capacitors are only sold in a restricted set of standard values, we used a parallel combination of several standard capacitors to make up the capacitances needed in all of the coupling units. The combinations used in the experiment for the coupling units t, v, −t, t_{AB}, g_{A}, g_{B} are shown in Supplementary Table 1. In addition to the variable inductors, removable 470 or 1800 pF capacitors are sometimes connected in parallel to certain coupling units to represent (k_{y}, k_{z}) points beyond the tuning range of the variable inductors alone. See Supplementary Table 2 for a complete list of component part numbers used in the experiment.
Offsetting calibration uncertainty: In the experiment, all variable inductor values are calibrated by a voltage divider as illustrated by Fig. 6d. As suggested by (19), they are crucially dependent on the known value of the calibrating resistance R_{t}. In particular, suppose that R_{t} has a manufacturing uncertainty ΔR_{t}. Then since the calibration voltage depends only on the product R_{t}G_{x}, the admittance of component G_{x} will also sustain a measurement error of ΔG_{x}/G_{x} = − ΔR_{t}/R_{t}. Since G_{x} is related to the frequency via \({G}_{x}=i\omega {C}_{x}+{(i\omega {L}_{x})}^{1}\) in (18), the effect of a nonzero ΔR_{t} can be offset by shifting the measurement frequency window by
where \({\omega }_{x}^{2}={({L}_{x}{C}_{x})}^{1}\) is the resonant frequency of coupling unit x. As such, calibration uncertainties can be offset by a small shift (in this case empirically determined to be all close to −60 kHz) in the measurement frequency up to leading order, allowing the drumhead region to still be faithfully mapped out.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
The work in Würzburg is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through ProjectID 258499086  SFB 1170 and through the WürzburgDresden Cluster of Excellence on Complexity and Topology in Quantum Matter – ct.qmat ProjectID 39085490  EXC 2147. T. Helbig was supported by a Ph.D. scholarship of the Studienstiftung des deutschen Volkes, Germany. X.Z. is supported by the National Natural Science Foundation of China (Grant No. 11874431), the National Key R&D Program of China (Grant No. 2018YFA0306800), and the Guangdong Science and Technology Innovation Youth Talent Program (Grant No. 2016TQ03X688). A.S., Y.S.A., and L.K.A. are supported by A*STARIRG (A1783c0011) and Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 2 grant (2018T21007). Open access funding provided by Projekt DEAL.
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C.H.L. conceptualized and initiated the project, designed the experiment and wrote most of the manuscript. T. Hofmann and T. Helbig performed the numerical simulations and provided circuit expertise. Y.L. and X.Z. provided support on the mathematical aspects. A.S. performed the experiment under the guidance of Y.S.A. L.K.A., M.G. and R.T. took on advisory roles and wrote parts of the manuscript. The manuscript reflects the contributions of all authors.
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Lee, C.H., Sutrisno, A., Hofmann, T. et al. Imaging nodal knots in momentum space through topolectrical circuits. Nat Commun 11, 4385 (2020). https://doi.org/10.1038/s41467020177161
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