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This is a reference to general scientific results: Quantum physics sets a speed limit to electronics.

This is a reference to general scientific results: Quantum physics sets a speed limit to electronics.

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This paper Fundamental Limits to Moore's Law discusses the question from the information thermodynamic perspective and offers rather shorter times. The Shannon-von Neumann-Landauer (SNL) expression together with the Heisenberg's Uncertainty Principle gives as time uncertainty:

$$ 0.04 ps $$

In their chapter 5: "5. Practical aspects Power consumption and speed are limited fundamentally by the devices, but practically by the electrical parasites, interconnects and chip architecture. This is the reason for the clock speed to have saturated at about 3 GHz for today's processors. All alternative ideas, like optical interconnects and more would see their limit in the domain conversion, which is limited by thermodynamics discussed here. The "2" in kBT ln2 can be made higher to, say, m, but that only pushes the limits by a factor of ln(m)/ln2. [7-8]"

This paper Fundamental Limits to Moore's Law discusses the question from the information thermodynamic perspective and offers rather shorter times. The Shannon-von Neumann-Landauer (SNL) expression together with the Heisenberg's Uncertainty Principle gives as time uncertainty:

$$ 0.04 ps $$

In their chapter 5: "5. Practical aspects Power consumption and speed are limited fundamentally by the devices, but practically by the electrical parasites, interconnects and chip architecture. This is the reason for the clock speed to have saturated at about 3 GHz for today's processors. All alternative ideas, like optical interconnects and more would see their limit in the domain conversion, which is limited by thermodynamics discussed here. The "2" in kBT ln2 can be made higher to, say, m, but that only pushes the limits by a factor of ln(m)/ln2. [7-8]"

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Ideal electromagnetic switch-on-process in a real electromagnetic cable.

data2a = Transpose[{autostep[[1, 1, 2]]["Coordinates"][1], autostep[[1, 1, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 1, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data2a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500]

to what is done by NDSolve

data2a = Transpose[{autostep[[1, 2, 2]]["Coordinates"][[1]], autostep[[1, 2, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 2, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data2a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500] 

to what is done by NDSolve

data3a = Transpose[{autostep[[1, 3, 2]]["Coordinates"][[1]], autostep[[1, 3, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 3, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data3a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500] 

to what is done by NDSolve

This remains looking artificial. This compare "Coordinates" to "ValueOnGrid". The patterns are synchronized but the pattern seems strangely sparse where the interesting integration occurs. This is the ploterror function rewritten for the presented code.

Ideal electromagnetic switch-on-process in a real electromagnetic cable.

data2a = Transpose[{autostep[[1, 1, 2]]["Coordinates"][1], autostep[[1, 1, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 1, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data2a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500]

to what is done by NDSolve

data2a = Transpose[{autostep[[1, 2, 2]]["Coordinates"][[1]], autostep[[1, 2, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 2, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data2a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500] 

to what is done by NDSolve

data3a = Transpose[{autostep[[1, 3, 2]]["Coordinates"][[1]], autostep[[1, 3, 2]]["ValuesOnGrid"]}]; Show[Plot[autostep[[1, 3, 2]][t], {t, 0, 1.5 10^-8}, PlotRange -> All], ListPlot[data3a, PlotStyle -> Red, PlotRange -> All], ImageSize -> 500] 

to what is done by NDSolve

This remains looking artificial. This compare "Coordinates" to "ValueOnGrid". The patterns are synchronized but the pattern seems strangely sparse where the interesting integration occurs. This is the ploterror function rewritten for the presented code.

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