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Timo Rahkonen and Janne P. Aikio
The purpose of this paper is to propose a method to reduce the non-linear distortion of a transistor to its input and output ports to aid distortion contribution analysis…
The purpose of this paper is to propose a method to reduce the non-linear distortion of a transistor to its input and output ports to aid distortion contribution analysis (DCA). This is especially needed when the internal structure of a device model is complex.
The non-linear distortion generated by all non-linear sources inside a device model are reduced to transistor i/o ports by LMSE fitting techniques. Simulations of an LDMOS power transistor are used to compare the reduced distortion results with the actual non-linear sources.
It is shown, that device models where the current sources are split by intermediate nodes cause superficial results, when distortion contributions are calculated as a superposition of contributions from individual non-linear sources. The proposed iterative fitting technique works.
Some non-quasistatic effects and the transfer functions from external terminals to internal controlling nodes are not covered.
The analysis is a step toward a generic non-linear distortion contribution simulation tool that would aid the designers to develop more linear analog circuits.
The concept of DCA itself is fairly new. This paper makes a step to represent the distortion sources in a canonical way.
Janne P. Aikio, Timo Rahkonen and Ville Karanko
The purpose of this paper is to propose methods to improve the least square error polynomial fitting of multi-input nonlinear sources that suffer from strong correlating…
The purpose of this paper is to propose methods to improve the least square error polynomial fitting of multi-input nonlinear sources that suffer from strong correlating inputs.
The polynomial fitting is improved by amplitude normalization, reducing the order of the model, utilizing Chebychev polynomials and finally perturbing the correlating controlling voltage spectra. The fitting process is estimated by the reliability figure and the condition number.
It is shown in the paper that perturbing one of the controlling voltages reduces the correlation to a large extend especially in the cross-terms of the multi-input polynomials. Chebychev polynomials reduce the correlation between the higher-order spectra derived from the same input signal, but cannot break the correlation between correlating input and output voltages.
Optimal perturbations are sought in a separate optimization loop, which slows down the fitting process. This is due to the fact that each nonlinear source that suffers from the correlation needs a different perturbation.
The perturbation, harmonic balance run and refitting of an individual nonlinear source inside a device model is new and original way to characterize and fit polynomial models.