Thermal Noise in Nonlinear Devices and Circuits Wolfgang Mathis and Jan Bremer Institute of Theoretical Electrical Engineering (TET) Faculty of Electrical Engineering und Computer Science University of Hannover Germany Content 1. Deterministic Circuit Descriptions 2. Stochastic Circuit Descriptions 3. Mesoscopic Approaches 4. Steps in Noise Analysis in Design Automation 5. Bifurcation in Deterministic Circuits 6. Bifurcation in Noisy Circuits and Systems 7. Examples 8. Conclusions 1. Deterministic Circuit Descriptions 2. Stochastic Circuit Descriptions 3. Mesoscopic Approaches 4. Steps in Noise Analysis in Design Automation 5. Bifurcation in Deterministic Circuits 6. Bifurcation in Noisy Circuits and Systems 7. Examples 8. Noise Analysis of Phase Locked Loops (PLL) 9. Conclusions 1. Deterministic Circuit Descriptions A. Meissner, 1913 Bob Pease, National Semiconductors Electrical and Electronic Circuits: The Ohm-Kirchhoff-Approach Real Circuit Circuit-Model Modelling b Partitioning b Models for Electronic Circuits O Electrical Circuits are defined in Z := (i, u ) : (i1 ,..., ib ; u1 ,..., ub ) Rb R b Space of all currents and Voltages Description of resistive NW elements: O := (i, u ) Rb Rb FR (i, u) 0, FR : Rb Rb R k ; k b Ohm Space b Description of connections: b K K := (i, u ) Rb Rb Ai 0 B u 0, ( A, B) exakt Kirchhoff Space S := K O State-Space of Electrical Circuits Dynamics electronic Circuits (Networks) S DAE System: dx f ( x, y ) dt 0 g ( x, y ) Capacitors Inductors Differentialalgebraic System DAE Systems consist of many e.g. (100-) thousand Equations numerical solutions necessary! Special Cases: State-Space Equations (ODEs) dx F ( x) dt Deterministic Description: ODEs Deterministic Description: ODEs d xi Fi ( x ) , dt x(t0 ) x0 where Fi : R n R (i 1, , n) and x Rn Initial value problems suitable for studying the quantitative behavior! x(t0 ) x0 Are initial value problems suitable for studying the qualitative behavior? Reformulation of the deterministic Dynamics Qualitative behavior: Considering a whole family of systems dxi Fi ( x), x0 x(t0 ) S IR dt Density Function p Dynamics of a Density Function p (Frobenius-Perron-Operator Pt ): p( x, t ) Pt ( p( x)) where n p Fi p t xi i 1 generalized Liouville equation p( x) Set of Initial Values 2. Stochastic Circuit Descriptions Thermal Noise in linear and nonlinear electrical Circuits with noise sources Noise Model Deterministic Approach Circuits Network Thermodynamics Circuit Equations Generalized Liouville Equation Generalization: (Non)linear Circuits including noise Deterministic Circuits Noisy Circuits Device Modelling Macroscopic Approach Mesoscopic Approaches Microscopic Approach Microscopic Approach: Statistical Physics e.g. RecombinationGenerationNoise drift movement Multi-Body System (approx. 1023 particles) C. Jungemann (see his talk this morning) 3. Mesoscopic Approaches The Langevin Approach: Stochastic ODE (SODE): dxi Fi ( x) ( x) i dt Fi : R n R (i 1, , n) and Noise sources as inputs x Rn i white noise and ( x) coupling coefficient Dynamics of the Density Function p: (mathematical equivalent to SODE ) n ( pFi ) p t xi i 1 1 n 2 ( 2 p) 2 i , j 1 xi x j Fokker-Planck equation Remarks: Fokker-Planck equation as modified generalized Liouville equation Langevin’s Approach: Noisy input Deterministic Circuit output (without inputs) Applications: e.g in Communication Systems Transmission of noisy signals through a deterministic channel (Mathematics: Transformation of stochastic processes) Physical Interpretation of SODE (Langevin, 1908) a) Linear Case (i white noise, konst.) dxi Aij x j dt j Average: dxi dt dxi Aij x j i dt j stoch. d xi dt A x ij j j i Aij x j i =0 j Conclusion: First Moment satisfies a determinstic differential equation b) Nonlinear Case (i white noise, konst.) dxi Fi ( x) dt Average: dxi dt stoch. (van Kampen, 1961) dxi Fi ( x) i dt =0 d xi Fj ( x) i Fj ( x) i dt Fj ( x ) Coupling of Moments Compare: In nonlinear systems Deterministic nonlinear System: Energetic Coupling of Frequencies Stochastic nonlinear System: Coupling of Moments of the probility density Sinusoidal input Alternative: Analyzing nonlinear circuits including noise Extraction of Noise Sources (then using the Langevin approach) Methods: • Calculation of desired spectra • Numerical Methods in Stochastic Differential Equations • Geometric Analysis of Stochastic Differential Equations Numerous papers However: Brillouin‘s Paradoxon of nonlinear electrical Circuits White noise White noise Stochastic Equation qu C du I s exp 1 dt q(2I s I) dw kT PN-Diode Average: (first moment) 2 q d 1 q 2 C u Is u u dt kT 2 kT : u q Thermodynamic Equilibrium: eq 2C Fluctuations-Voltage-Converter „A diode can rectify its own noise“ Contradiction against the second law of thermodynamics 1. Motivation 5 (white noise sources in device models are forbidden: …., Weiss, Mathis, Coram, Wyatt (MIT)) Nonlinear Electronic Circuits Electrical current is related to noisy electron transport internal noise (cannot switched off) in nonlinear systems (electrical circuits) Drift Movement No systematic extraction of deterministic equations The entire behavior has to be described as a stochastic process phys. Assumption: description as a Markov process Mesoscopic Approach based on statistical thermodynamics “First Principle” Mesoscopic Approach for Circuits with Internal Noise Starting Point: Markovian Stochastic Processes x(t , ) are defined by the Chapman-Kolmogorov Equation (Integral equation for the transition probability density) Types of Markov Processes time domain Stochastic differential Equation (SODE) probability density domain mathematical equivalent! Fokker-Planck equation more general partial differential equations for the probability density domain General solutions of the Chapman-Kolmogorov equation by the Kramers-Moyal series Derivation of the Kramers-Moyal Coefficients for nonlinear systems by Nonlinear Nonequilibrium Statistical Thermodynamics (Stratonovich): „Thermal noise“: In thermodynamical equilibrium ( (stable) ) the equilibrium density function is known: peq e W X kT Perturbation analysis for calculating coefficients „Irreversible Statistical Thermodynamics of Circuits“ Weiss; Mathis (1995-2001), Dissertation (Weiss) 1999 However: Restricted to reciprocal circuits (no transistors!) Statistical Thermodynamics of Thermal Noise in Nonlinear Circuit Theory Using Stratonovich‘s Approach: Basic is the Markov Assumption Kramers-Moyal Equation p(X, t ) (1) m t m1 m! m K 1m (X) p(X, t ) 1 m X 1 X m determination ? Circuit Equations d X K (X) o(kT) dt Thermodynamic Equilibrium p eq (X) exp( WX / kT) Detailed Balance (Reciprocity) Nonlinear „distributed“ Generalization of the Nyquist’s Formula Nonlinear Circuits (Weiss und Mathis (1995-1999)) Starting Point: 3.1 Anwendung: Vollständige nichtlineare Netzwerke Complete Reciprocal Circuits: Brayton-Moser Description Network Equations L (I ) dI dt M(I, U) I dU M(I, U) dt U Mixed Potential M: Dissipation C (U ) - Reciprocal - DAE Description (Index 1) M(I,U) = F(I) - G(U) + (I,U) Linear Approximation: X (0) Y , Quadratic Approximation: X (0) Y , 2 2 3 1/ 2 (0) Y Y , not of Fokker-Planck type Cubic Approximation: X (0) Y , 1 2 1 (0) Y Y (0) Y Y Y 6 , Noise cannot be determined thermodynamical! , Our Approach of Noise Spectra Calculations Physical Assumptions Current-Voltage Relation Circuit Topology Stratonovich Machine Correct Noise Spectra (if the physical assumptions valid) Note: Assumptions are not satisfied if non-thermal effects are included (hot electron effects) The “Thermodynamic Window” of a Circuit Microscopic Behavior Currents and Voltages Linear RC Networks: Classical Result Stochastic Diff.Equ. (NoiseSource) C du K(u ) dt Signal S I dw Noise equivalent SODE Fokker-PlanckEquation (distributed Noise) K ( U) 2 p( U, t ) SI 1 p( U, t ) p( U, t ) 2 2 t U C 2 U C Network Equation our approach K(U) = - U / R Thermodynamic Equilibrium p eq ( U) exp( WC / kT) Nyquist‘s Formular (linear approximation) S I 2kTG Dissipation (G 1 / R) Fluctuation Some Results: Nonlinear RC Network (Linear-quadratic Approximation) 3.2 Nichtlineares RC Netzwerk: Linear-quadratische Näherung Network ODE dU dg(0) 1 d 2 g ( 0) 2 C U U 2 dt dU 2 dU our approach (equivalent SODE) dg(0) 1 d 2g (0) SODE C du u du 2 du 2 dg(0) 1 d 2g (0) 2 kT u C dt 2kT du 2 du 2 u dw u 2 kT / C that is Short Circuit Generalized Nyquist Formula dg(0) 1 d 2 g (0) SI 2kT U 2 2 dU dU Results about Thermal +Noise in Semiconductor Devices (Weiss, Mathis: IEEE Electron Devices Letters 1999) 1. Schottky Diode our approach Shot Noise! Note: Shot noise has a thermal background (see Schottky (1918)) 2. MOSFET (simple model) MOSFET: 1 2 I D U GS U t U DS U DS 2 our approach known from microscopic analysis (see textbooks): SI 2 kT U GS U t 1 U DS known from S I, therm 2 kT U GS 2 2 U DS U DS 1 1 U GS U t 3 U GS U t 2 Ut 1 U DS 1 2 U GS U t 1 2 kT U GS U t U DS O U DS2 2 O (U S I therm DS 2 ) FET: 3. JFET 3/ 2 3/ 2 2 U diff U GS U DS U diff U GS I D g 0 U DS 1/ 2 3 U0 our approach known from microscopic analysis (e.g. van der Ziel (1962): SI 2 kT g 0 1 SI, therm 2 kT g 0 x U diff U GS U DS U0 4 U diff U GS U 0 4 3/ 2 1 x y3 / 2 x 2 y 2 3 2 , 2 3/ 2 3/ 2 xy x y 3 xy U diff U GS U DS U U GS , y diff U0 U0 U DS 1 U diff U GS 2 kT g S I Stherm 0 I , therm U0 4 U diff U GS U 0 O (U 2 ) DS O (U 2 DS ) 4. Steps of Noise Analysis in Design Automation • First Generation: LTI-Noise Models Linear Noise Analysis based on Schottky-Johnson-Nyquist (Rohrer, Meyer, Nagel: 1971 - …) Idea: „Linearization with respect to an operational point (constant solution)“ State Space Small-signal noise models do not work if e.g. bias changes occur, oscillators, more general nonlinear circuits • Second Generation: LPTV Models Variational Linear Noise Analysis of Periodical Systems (Hull, Meyer (1993), Hajimiri, Lee (1998)) Idea: „Linearization with respect to a periodic solution“ State Space Useful for periodic driven systems, however heuristic assumptions and concept will be needed for oscillators (Leeson‘s formula) • Third Generation: SDAE Models Noise Analysis by Stochastic Differential Algebraic Equations (Kärtner (1990), Demir, Roychowdhury (2000)) DAE System: dx f ( x, y ) dt 0 g ( x, y ) Differential- algebraic System + Noise (stochastic processes) dx B( x) g ( x) f ( x) (t ) dt Systematic Results in Phase Jitter of Oscillators as well as other nonlinear systems (e.g. PLL), however the onset of oscillations cannot described 5. Bifurcation in Deterministic Circuits Given: Choice: f osz , Cgs, Cds, RL, y22; CG, CL (influence of Cgs and Cds „small“) L gm RL Linearization? Cgs//CG Cds//CL FET Colpitts Oscillator Theorem of Hartman-Grobman: What is happened if the circuit is non-hyperbolic? 1 0.8 0.6 The dynamical behavior of state space equations is related to the dynamics of the „linearized“ equations in hyperbolic cases. Barkhausen or Nyquist Criteria x I C 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 0 x x 2 4 6 8 10 12 14 16 18 20 In certain cases Limit Cycles can be observed Example: Sinusoidal Oscillators damping term x ( x 2 x 2 1) x x 0 Obvious solution: x (t ) cos t (or x (t ) sin t ) State space interpretation: x Type of damping positive Periodic Solution ( x 2 x 2 1) 0 x negative Analysis of Systems with Limit Cycles Idea: (Poincaré; Mandelstam, Papalexi - 1931) Embedding of an oscillator (equation) into a parametrized family of oscillator (equations) Example: Van der Pol equation ( x 2 x 2 1) x x 0 x ( x 2 x 2 ) x x 0 x embedding with Andronov-Hopf Bifurcation x Stable equilibrium point x Limit Cycle State Space Bifurcation Point Poincaré-Andronov-Hopf Theorem (1934,1944) Let x f ( x, ), with f (0, ) 0 f : Rn R Rn for all in a neighborhood of 0. If • the Jacobi matrix Dx f (0,0) includes a pair of imaginary eigenvalues 1, 2 j • the other eigenvalues have a negative real part d • d 1 ( ) 0 0 • the equilibrium point for 0 asymptotic stable Then there exist • an asymptotic stable equilibrium point for ( 1 ,0) • a stable limit cycle for (0, 2 ) Transient Behavior of a Sinusoidal Oscillator (Center Manifold Mc) Concept for Analysis of Practical Oscillators x f ( x, ), f : IR n IR IR n • Transformation of the linear part: Jordan Normal Form • Transformation of the Equations: Center Manifold • Transformation of the reduced Equations: Poincaré-Normal Form • Averaging Symbolic Analysis (MATHEMATICA, MAPLE) 6. Bifurcation in Noisy Circuits and Systems Different Concepts: I) The physical (phenomenological) approach (e.g. van Kampen) Special Case: Dynamics in a Potential U(x) U(x) U(x) initial P.D.F. Behavior of P.D.F. p near a stable equilibrium point initial P.D.F. ? Behavior of P.D.F. p near a unstable equilibrium point? Dynamical Equation: Fokker-Planck stationary dx U '( x) dt dxt xt xt3 dt dWt SODE p 2 p U '( x) p 2 t x x ! 2 p 0 U '( x) p 2 x x 4 x e.g. U ( x) x 2 2 pstat ( x) C e U ( x) 0 single well >0 double well x4 x 2 2 pstat ( x) C e 0 unimodal 0 bimodal For 0 the equilibrium P.D.F. p(x) changes its type It is called P-bifurcation (e.g. L. Arnold) Obvious disadvantage: (Zeeman, 1988) “It seems a pity to have to represent a dynamical system by y static picture”* II) The mathematical approach (e.g. L. Arnold) Observation:** One-one correspondence: stationary P.D.F. and invariant measures (I.M.) Consider more general invariant measures (if exist) D(ynamical)-Bifurcation Point D of a family of stochastic dynamics ( w.r.t. “Near” D we have another I.M. * Arnold, p. 473 ** Arnold, p. 473 R) D with a ergodic I.M. with Question: Is there any relationship between these types of bifurcation In general, there is not! Our example: (above) The corresponding invariant measure to x2 pstat ( x) C e x4 2 0 unimodal 0 bimodal There is no D-Bifurcation is unique* Remark: There are cases with D-bifurcation but no P-bifurcation* * Arnold, p. 476 Case 1: Pitchfork Bifurcation dxt xt x dt xt dWt 3 t Invariant Measures q q 0 D P D 0 P q 2 2 q Case 2: Andronov Bifurcation 0 dxt 3 2 x1 x1 x 0 dt 1 0 0 xt dt xt dWt 1 0 invariant measures D1 D2 P P 0 D 1 D 2 0 unstable 0 stable 0 saddle 1 stable 1 stable 2 saddle Main Questions: There are stochastic generalizations of geometric theorems • Hartman-Grobman Global H-G: Wanner, 1995 (local H-G: still open question) • Poincaré-Andronov-Hopf Arnold, Schenk-Hoppe Namachchivaya 1996 • Center Manifold Boxler 1989, Arnold, Kadei 1993 • Poincarè Normal Form Elphick et al. 1985, C.+G. Nicolis 1986 (physics) Namachchivaya et al. 1991 Arnold, Kedai 1993, 1995 Arnold, Imkeller 1997 Remark: Until now a research program (Arnold, ...) 7. Examples Meissner oscillator and van der Pol’s equation Meissner‘s Tube Oscillator k1 , k2 , k3 L0 , M uBE C 1 L0C 2 0 R duBE d 2uBE R 2 2 2 M k 2 k u 3 k u 0 1 2 BE 3 BE 0 u BE 0 2 dt L0 dt Normalization and Scaling: ( x1 uBE , x2 uBE ) 1 0 0 x1 d x1 2 2 Mk R 2 2 2 2 k Mx x 3 k Mx x x dt x2 0 0 1 0 3 1 2 2 0 2 1 2 L 0 Noisy frequency: 02 ( :white noise) and k2 0 (using results from Arnold, Imkeller 1997) x1 0 d x2 1 x1 0 0 0 x x x 2 dt x dWt 2 1 2 1 0 where := 02 Mk1 R , 3 02 k3 M 1, Wt : Wiener process L0 Linearization: x1 0 d x2 1 x1 0 0 x dt x dWt 2 1 0 Center Manifold: If 0 02 ! , < 4 d : 2 / 4 R 2-dim. Center space and no stable space Normal Form in Resonant Case: (polar coordinates) Solution of stochastic cohomology equation results (see approach: Arnold, Imkeller, 1997) Meissner oscillator with nonlinear capacitor k1 , k2 , k3 L0 , M uBE C q 02 1 L0C0 R duBE d 2uBE R 2 3 2 3 M k 2 k u 3 k u u u 0 1 2 BE 3 BE 0 BE BE 0 2 dt L0 dt Duffing-van der Pol equation noisy case x1 0 d x2 0 1 x1 0 0 x x3 x x 2 dt x dWt 2 1 1 2 1 0 Normal Form in Resonant Case: (polar coordinates) Solution of stochastic cohomology equation results (results: Arnold, Imkeller, 1997) 5rt 2 rt 2rt 2 1 3 2 d rt rt rt dt 2 2rt 4 d sin 2t sin 4t rt 2 cos 2t dWt 2 8 d d 2 d 6 3 2 1 d t d rt dt 4 d 2 d 3rt 2 5rt 2 rt 2 3rt 2 1 1 cos 2 cos 4 sin 2 t t t 2 2 dWt 2 2 2 d d d d Ljapunov-Exponents: PardouxWihstutz formula 1988 2 1,2 , 2 O 4 2 8 d 0 0 0 0 stochastic P- Andronov-Hopf bifurcation first stochastic D-Andronov-Hopf bifurcation 0 Stability is lost determinstic Andronov-Hopf bifurcation 2 0, 0 1 0, 0 8. Noise Analysis of Phase Locked Loops (PLL) Phase Detector s (t ) Lowpass Filter F(s) P r (t ) VCO Voltage Controlled Oscillator Equivalent Base-band Modell: t (t ) + x(t ) e (t ) A sin(.) K ˆ t F(s) F(s)=1 t d 0 d d K A f (t ) sin d dt dt 0 t d d K A sin t , dt dt State Space Equation 2nd order PLL with ideal Integrator Equivalent Base-band Modell System State Space Equation: d 2 sin x1 d n d x1 sin d Noisy state equation Formally, one obtains: noise But: How do we interpret this equation, if n’(t) is not exactly known? - need generic results Noisy state equation Necessary Assumption: PLL-bandwidth is small compared with BW of the noise-process - sufficient to model n’(t) as white noise again - rewrite state equation as SDE Normalized SDE After time-normalization and introducing parameters from linear PLL noise theory, one obtains: Interpretation: • SDE in the Stratonovich sense • dw() = (t)d: increment of a normalized Wiener process • BL: loop noise bandwidth, : frequency offset between input and VCO output, : SNR in the loop The Euler-Maruyama scheme (1) • based on Ito-Taylor expansion ) consistent with Ito-calculus • Ito stochastic integrals ) evaluate Riemann sum approximation at lower endpoint Consider the scalar Ito-SDE And the corresponding Euler-Maruyama scheme The Euler-Maruyama scheme Consistency with Ito-calculus Noise term in the EM scheme approximates the Ito stochastic integral over interval [tn, tn+1] by evaluating its integrand at the lower end point of this interval, that is Phase-acquisition time • Time to reach locked state from an initial state Transient PDF – Lock-in Meantime between cycle slips Simulation approach • numerically solve the SDE using the Euler-Maruyama scheme • estimate probabilities using relative frequencies • verify the accuracy with the results from the Fokker-Planck method • a relative tolerance level of 5% was allowed Still no simulation required more than 5 minutes on a standard PC 9. Conclusions • Determinstic and stochastic behavior are related in time domain and density function domain • Physical description of noise with a nonlinear Langevin equation fails with respect to its physical interpretation • For thermal noise in nonlinear reciprocal circuits a welldefined theory is available (L.E. as approx.) • For nonhyperbolic circuits (e.g. oscillators) first concepts for a geometric theory is available There is a difference between P- and D-Bifurcation • • Stochastic D-Andronov-Hopf theorem is illustrated by means of versions of a Meissner oscillator circuit 10. References TET References: • • • • • • • • • • • • B. Beute, W. Mathis, V. Markovic: Noise Simulation of Linear Active Circuits by Numerical Solution of Stochastic Differential Equations. Proceedings of the 12th International Symposium on Theoretical Electrical Engineering (ISTET), 6 - 9 July 2003, Warsaw, Poland Mathis, W.; M. Prochaska: Deterministic and Stochastic Andronov-Hopf Bifurcation in Nonlinear Electronic Oscillations, Proceedings of the 11th workshop on Nonlinear Dynamics of Electronic Systems (EDES), 161164, 18-22 May 2003, Scuols, Schweiz W. Mathis: Nonlinear Stochastic Circuits and Systems – A Geometric Approach. Proc. 4th MATHMOD, 5-7 Februar 2003, Wien (Österreich) L. Weiss: Rauschen in nichtlinearen elektronischen Schaltungen und Bauelementen - ein thermodynamischer Zugang. Berlin; Offenbach: VDE Verlag, 1999. Also: Ph.D. thesis, Fakultät Elektrotechnik, Otto-vonGuericke-Universität Magdeburg, 1999. L. Weiss, W. Mathis: A thermodynamic noise model for nonlinear resistors, IEEE Electron Device Letters, vol. 20, no. 8, pp. 402-404, Aug. 1999. L. Weiss, W. Mathis: A unified description of thermal noise and shot noise in nonlinear resistors (invited paper), UPoN'99, Adelaide, Australia, July 11-15, 1999. L. Weiss, D. Abbott, B. R. Davis: 2-stage RC ladder: solution of a noise paradox, UPoN'99, Adelaide, Australia, July 11-15, 1999. W. Mathis, L. Weiss: Physical aspects of the theory of noise of nonlinear networks, IMACS/CSCC'99, Athens, Greece, July 4-8, 1999. W. Mathis, L. Weiss: Noise equivalent circuit for nonlinear resistors, Proc. ISCAS'99, vol. V of VI, pp. 314-317, Orlando, Florida, USA, May 30 - June 2, 1999. L. Weiss, W. Mathis: Thermal noise in nonlinear electrical networks with applications to nonlinear device models, Proc. IC-SPETO'99, pp. 221-224, Gliwice, Poland, May 19-22, 1999. L. Weiss, W. Mathis: Irreversible Thermodynamics and Thermal Noise of Nonlinear Networks, Int. J. for Computation and Mathematics in Electrical and Electronic Engineering COMPEL, vol. 17, no. 5/6, pp. 635648, 1998. W. Mathis, L. Weiss: Noise Analysis of Nonlinear Electrical Circuits and Devices. K. Antreich, R. Bulirsch, A. Gilg, P. Rentrop (Eds.): Modling, Simulation and Optimization of Integrated Circuits. International Series of Numerical Mathematics, Vol. 146, pp. 269-282, Birkhäuser Verlag, Basel, 2003 • L. Weiss, M.H.L. Kouwenhoven, A.H.M van Roermund, W. Mathis: On the Noise Behavior of a Diode, Proc. Nolta'98, vol. 1 of 3, pp. 347-350, Crans-Montana, Switzerland, Sept. 14-17, 1998. • L. Weiss, W. Mathis: N-Port Reciprocity and Irreversible Thermodynamics, Proc. ISCAS'98, vol. 3 of 6, pp. 407410, Monterey, California, USA, May 31 - June 03, 1998. * • L. Weiss, W. Mathis, L. Trajkovic: A Generalization of Brayton-Moser's Mixed Potential Function, IEEE CAS I, vol. 45, no. 4, pp. 423-427, April 1998. • L. Weiss, W. Mathis: A Thermodynamical Approach to Noise in Nonlinear Networks, International Journal of Circuit Theory and Applications, vol. 26, no. 2, pp. 147-165, March/April 1998. Further references: • Langevin, P., Comptes Rendus Acad. Sci. (Paris) 146, 1908, 530 • W. Schottky, W.: Über spontane Stromschwankungen in verschiedenen Elektrizitätsleitern, Ann. d. Phys. 57, 1918, 541-567 • J. Guckenheimer; P. Holmes: Nonlinear oscillations, dynamical systems, and bifurcation of vector fields. Springer-Verlag, Berlin-Heidelberg 1983 • N.G. van Kampen: Stochastic Processes in Physics and Chemistry. North Holland, Amsterdam 1992 • R.L. Stratonovich.: Nonlinear Thermodynamics I. Springer-Verlag, Berlin-Heidelberg, 1992 • L. Arnold: The unfoldings of dynamics in stochastic analysis. Comput. Appl. Math. 16, 1997, 3-25 • W. Mathis: Historical remarks to the history of electrical oscillators (invited). In: Proc. MTNS-98 Symposium, July 1998, IL POLIGRAFO, Padova 1998, 309-312. • L. Arnold; P. Imkeller: Normal forms for stochastic differential equations. Probab. Theory Relat. Fields 110, 1998, 559-588 • L. Arnold: Random dynamical systems. Berlin-Heidelberg-New York 1998 • W. Mathis: Transformation and Equivalence. In: W.-K. Chen (Ed.): The Circuits and Filters Handbook. CRC Press & IEEE Press, Boca Raton 2003