A performance estimation method includes determining, for a device response that is dependent on first factors and second factors, a plurality of response distributions including at least one of: a measured response distribution of a production set corresponding to a setting of the first factors, the production set including a plurality of manufactured units; a response distribution simulated in accordance with a simulation model and with a combined factor setting of the first factors and the second factors; and a response distribution estimated in accordance with the combined factor setting and in accordance with a response prediction model relating the device response to the first factors and to the second factors. The method also includes estimating, in accordance with the plurality of response distributions, a performance metric prediction model relating a performance metric to the first factors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A performance estimation method comprising: determining, for a device response that is dependent on first factors and second factors, a plurality of response distributions comprising at least one of: a measured response distribution of a production set corresponding to a setting of the first factors, the production set comprising a plurality of manufactured units; a response distribution simulated in accordance with a simulation model and with a combined factor setting of the first factors and the second factors; and a response distribution estimated in accordance with the combined factor setting and in accordance with a response prediction model relating the device response to the first factors and to the second factors; and estimating, in accordance with the plurality of response distributions, a performance metric prediction model relating a performance metric to the first factors. 2. The method of claim 1, further comprising: simulating values of the device response in accordance with the simulation model and in accordance with settings of N factors comprising the first factors and the second factors; determining linear regression coefficients of the response prediction model in accordance with the simulated values of the device response; generating, for each of a plurality of settings of the first factors, a respective plurality of random values of the second factors to determine combined factor settings; and estimating the plurality of response distributions in accordance with the combined factor settings. 3. The method of claim 2, further comprising: selecting the settings of the N factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion; and selecting the plurality of settings of the first factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion, wherein: the response prediction model further comprises a second-order polynomial linear regression model; and the performance metric prediction model further comprises a second-order polynomial linear regression model. 4. The method of claim 1, wherein: the first factors comprise at least one of a software setting, a pre-determined temperature, a supply voltage, and a design component value; and the second factors comprise at least one of a production process parameter, a load characteristic, a randomly varying electrical input, and a randomly varying temperature. 5. The method of claim 1, wherein: the estimating the performance metric prediction model comprises determining linear regression coefficients in accordance with the response distributions, an upper limit for the response distributions, and a lower limit for the response distributions; the performance metric comprises one of process capability index, design index, failure probability, and worst case distance; and the response distributions are multimodal. 6. The method of claim 1, further comprising: selecting a candidate design; estimating a performance metric value in accordance with the candidate design and the performance metric prediction model; verifying the performance metric value satisfies a performance requirement; and manufacturing device units in accordance with the candidate design. 7. The method of claim 6, wherein: the manufacturing the device units comprises: generating a mask for an integrated circuit in accordance with the candidate design, the candidate design comprising a formal description of an electronic circuit; and manufacturing the integrated circuit in accordance with the mask. 8. A performance estimation circuit configured to: determine, for a device response that is dependent on first factors and second factors, a plurality of response distributions comprising at least one of: a measured response distribution of a production set corresponding to a setting of the first factors, the production set comprising a plurality of manufactured units; a response distribution simulated in accordance with a simulation model and with a combined factor setting of the first factors and the second factors; and a response distribution estimated in accordance with the combined factor setting and in accordance with a response prediction model relating the device response to the first factors and to the second factors; and estimate, in accordance with the plurality of response distributions, a performance metric prediction model relating a performance metric to the first factors. 9. The circuit of claim 8, further configured to: simulate values of the device response in accordance with the simulation model and in accordance with settings of N factors comprising the first factors and the second factors; determine linear regression coefficients of the response prediction model in accordance with the simulated values of the device response; generate, for each of a plurality of settings of the first factors, a respective plurality of random values of the second factors to determine combined factor settings; and estimate the plurality of response distributions in accordance with the combined factor settings. 10. The circuit of claim 9, further configured to: select the settings of the N factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion; and select the plurality of settings of the first factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion, wherein: the response prediction model further comprises a second-order polynomial linear regression model; and the performance metric prediction model further comprises a second-order polynomial linear regression model. 11. The circuit of claim 8, wherein: the first factors comprise at least one of a software setting, a pre-determined temperature, a supply voltage, and a design component value; and the second factors comprise at least one of a production process parameter, a load characteristic, a randomly varying electrical input, and a randomly varying temperature. 12. The circuit of claim 8, further configured to: determine linear regression coefficients of the performance metric prediction model in accordance with the response distributions, an upper limit for the response distributions, and a lower limit for the response distributions; wherein: the performance metric comprises one of process capability index, design index, failure probability, and worst case distance; and the response distributions are multimodal. 13. The circuit of claim 8, further configured to: select a candidate design; estimate a value of the performance metric in accordance with the candidate design and the performance metric prediction model; and verify the performance metric value satisfies a performance requirement. 14. The circuit of claim 8, comprising a processor. 15. A method for fabricating an integrated circuit, comprising: selecting a candidate design; determining a plurality of response distributions each corresponding to a respective setting of first factors and a respective plurality of random values of second factors; estimating, in accordance with the plurality of response distributions, a performance metric prediction model relating a performance metric to the first factors; and estimating, in accordance with the performance metric prediction model, a performance metric value of the candidate design. 16. The method of claim 15, further comprising at least one of: verifying satisfaction of a performance requirement by the performance metric value; and adjusting, in accordance with the performance metric prediction model, a setting of the candidate design. 17. The method of claim 15, further comprising: generating, in accordance with candidate settings, a first design from which the integrated circuit may be manufactured, wherein: the candidate design comprises the first design; the first design comprises one of a circuit layout and a circuit description database; the candidate settings comprise a value of a component of the integrated circuit; and the integrated circuit component comprises one of a resistor, an inductor, a capacitor, a diode, a transformer, an electronic oscillator, an analog filter, a digital filter, an operational amplifier, a power amplifier, and a transistor. 18. The method of claim 15, wherein the plurality of response distributions comprise at least one of: a measured response distribution of a production set corresponding to a setting of the first factors, the production set comprising a plurality of manufactured units; a response distribution simulated in accordance with a simulation model and with a combined factor setting of the first factors and the second factors; and a response distribution estimated in accordance with the combined factor setting and in accordance with a prediction model of a response of the integrated circuit, the response prediction model relating the integrated circuit response to the first factors and to the second factors. 19. The method of claim 18, further comprising: simulating values of a response of the integrated circuit in accordance with the simulation model and in accordance with settings of N factors comprising the first factors and the second factors; determining linear regression coefficients of the response prediction model in accordance with the simulated values of the electronic circuit response; generating, for each of a plurality of settings of the first factors, a respective plurality of random values of the second factors to determine combined factor settings; and estimating the plurality of response distributions in accordance with the combined factor settings. 20. The method of claim 19, further comprising: selecting the settings of the N factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion; and selecting the plurality of settings of the first factors in accordance with one of an A-optimal criterion, a D-optimal criterion, and a G-optimal criterion, wherein: the response prediction model further comprises a second-order polynomial linear regression model; and the performance metric prediction model further comprises a second-order polynomial linear regression model. 21. The method of claim 18, further comprising: determining linear regression coefficients of the performance metric prediction model in accordance with the response distributions, an upper limit for the response distributions, and a lower limit for the response distributions; wherein: the second factors comprise at least one of a production process parameter, a load characteristic, a randomly varying electrical input, and a randomly varying temperature; the performance metric comprises one of process capability index, design index, failure probability, and worst case distance; and the response distributions are multimodal. 22. The method of claim 21, wherein: the response prediction model further comprises first-order interactions; and the performance metric prediction model further comprises first-order interactions.