Abstract: Disclosed is a near-threshold cell circuit delay model, where obtaining parameters includes obtaining process parameters, current parameters and delay parameters with slow input transition; judging a cell circuit type includes judging whether a cell circuit is an inverter, a stacked structure cell or a parallel structure cell, calculating currents and a current integral according to the cell circuit type, calculating a mean value, a variance and a skewness of a logarithm of the current sum, and calculating a mean value and a variance of an equivalent threshold voltage; judging a delay type includes calculating an overshoot time and a delay according to the cell circuit type, comparing the magnitude relationship among an input transition time, the overshoot time and the delay, and judging whether the delay type is ultra-fast input, fast input or slow input; establishing a cell circuit nominal delay model is establishing the cell circuit nominal delay model according to the cell circuit type and the delay type,

Abstract: A method for predicting the fluctuation of circuit path delay on the basis of machine learning, comprising the following steps: S1: selecting suitable sample characteristics by means of analyzing the relationship between circuit characteristics and path delay; S2: generating a random path by means of enumerating values of randomized parameters, acquiring the maximum path delay by means of performing Monte Carlo simulation on the random path, selecting a reliable path by means of the 3? standard, and using the sample characteristics and path delay of the reliable path as a sample set (D); S3: establishing a path delay prediction model, and adjusting parameters of the model; S4: verifying the precision and stability of the path delay prediction model; S5: obtaining the path delay.

Abstract: An exemplary, highly integrated, SPM-based system for measuring the conductivity and/or force of substance under programmable engaging/stretching processes is described. A sample bias is applied across two electrodes. A substance to be measured is sandwiched between them. A first electrode is first brought relative to a second electrode (engaging) in programmable pathways that can be described as stretching distance versus time curves. The process of engaging the electrodes continues until a certain current reached, a certain force reached and whichever case happens first. The electrodes are then separated (stretching) in programmable pathways that can be described as stretching distance versus time curves. A periodic modulation can be applied to the engaging/stretching process to realize different stretch pathways. The sample bias across the electrodes is kept constant or swept in a programmable shape over time, described as a voltage-versus time curve.

Abstract: An exemplary, highly integrated, SPM-based system for measuring the conductivity and/or force of substance under programmable engaging/stretching processes is described. A sample bias is applied across two electrodes. A substance to be measured is sandwiched between them. A first electrode is first brought relative to a second electrode (engaging) in programmable pathways that can be described as stretching distance versus time curves. The process of engaging the electrodes continues until a certain current reached, a certain force reached and whichever case happens first. The electrodes are then separated (stretching) in programmable pathways that can be described as stretching distance versus time curves. A periodic modulation can be applied to the engaging/stretching process to realize different stretch pathways. The sample bias across the electrodes is kept constant or swept in a programmable shape over time, described as a voltage-versus time curve.