Automated analog and mixed-signal circuit design and validation

1. A method comprising:

receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component, the first electrical component and the second electrical component arranged in a first topology;

identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology;

identifying sub-circuit physical parameter values associated with the first electrical component and the second electrical component of the first sub-circuit;

determining a set of sub-circuit performance parameter values for the first sub-circuit based on a first machine learning (ML) model of the first sub-circuit and the identified sub-circuit physical parameter values;

converting the identified first sub-circuit to a second sub-circuit for a second process technology based on the determined set of sub-circuit performance parameter values; and

outputting the second sub-circuit.

2. The method of claim 1 , wherein converting the identified first sub-circuit to the second sub-circuit comprises:

determining a second set of sub-circuit physical parameters associated with a third electrical component and a fourth electrical component of the second sub-circuit based on a second ML model, for the second process technology, and the set of sub-circuit performance parameter values; and

associating sub-circuit physical parameters of the second set of sub-circuit physical parameters with the third electrical component and the fourth electrical component of the second sub-circuit.

3. The method of claim 2 , wherein the third electrical component and the fourth electrical component correspond to the first electrical component and the second electrical component, respectively.

4. The method of claim 2 , wherein the first ML model and the second ML model comprise neural networks.

5. The method of claim 1 , wherein the second process technology comprises a second semiconductor manufacturing process associated with smaller electrical components as compared to the first process technology.

6. The method of claim 1 , further comprising verifying the second sub-circuit based on a circuit simulation of the second sub-circuit.

7. The method of claim 1 , wherein sub-circuit performance parameters of the set of sub-circuit performance parameters are determined based on a type of the identified first sub-circuit.

8. The method of claim 1 , wherein identifying the first sub-circuit is based on a set of rules.

9. A non-transitory program storage device comprising instructions stored thereon to cause one or more processors to:

receive a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component, the first electrical component and the second electrical component arranged in a first topology;

identify the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology;

identify sub-circuit physical parameter values associated with the first electrical component and the second electrical component of the first sub-circuit;

determine a set of sub-circuit performance parameter values for the first sub-circuit based on a first machine learning (ML) model of the first sub-circuit and the identified sub-circuit physical parameter values;

convert the identified first sub-circuit to a second sub-circuit for a second process technology based on the determined set of sub-circuit performance parameter values; and

output the second sub-circuit.

10. The non-transitory program storage device of claim 9 , wherein the instructions for converting the identified first sub-circuit to the second sub-circuit comprises instructions to cause the one or more processors to:

determine a second set of sub-circuit physical parameters associated with a third electrical component and a fourth electrical component of the second sub-circuit based on a second ML model, for the second process technology, and the set of sub-circuit performance parameter values; and

associate sub-circuit physical parameters of the second set of sub-circuit physical parameters with the third electrical component and the fourth electrical component of the second sub-circuit.

11. The non-transitory program storage device of claim 10 , wherein the third electrical component and the fourth electrical component correspond to the first electrical component and the second electrical component, respectively.

12. The non-transitory program storage device of claim 9 , wherein the first ML model and second ML model comprise neural networks.

13. The non-transitory program storage device of claim 9 , wherein the second process technology comprises a second semiconductor manufacturing process associated with smaller electrical components as compared to the first process technology.

14. The non-transitory program storage device of claim 9 , wherein the instructions further comprise instructions to cause the one or more processors to verify the second sub-circuit based on a circuit simulation of the second sub-circuit.

15. The non-transitory program storage device of claim 9 , wherein performance parameters of the set of performance parameters are determined based on a type of the identified first sub-circuit.

16. The non-transitory program storage device of claim 9 , wherein identifying the first sub-circuit is based on a set of rules.

17. An electronic device, comprising:

a memory; and

one or more processors operatively coupled to the memory, wherein the one or more processors are configured to execute instructions causing the one or more processors to:

receive a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component, the first electrical component and the second electrical component arranged in a first topology;

identify the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology;

identify sub-circuit physical parameter values associated with the first electrical component and the second electrical component of the first sub-circuit;

determine a set of sub-circuit performance parameter values for the first sub-circuit based on a first machine learning (ML) model of the first sub-circuit and the identified sub-circuit physical parameter values;

convert the identified first sub-circuit to a second sub-circuit for a second process technology based on the determined set of sub-circuit performance parameter values; and

output the second sub-circuit.

18. The electronic device of claim 17 , wherein the instructions for converting the identified first sub-circuit to the second sub-circuit comprises instructions to cause the one or more processors to:

determine a second set of sub-circuit physical parameters associated with a third electrical component and a fourth electrical component of the second sub-circuit based on a second ML model, for the second process technology, and the set of sub-circuit performance parameter values; and

associate sub-circuit physical parameters of the second set of sub-circuit physical parameters with the third electrical component and the fourth electrical component of the second sub-circuit.

19. The electronic device of claim 18 , wherein the third electrical component and the fourth electrical component correspond to the first electrical component and the second electrical component, respectively.

20. The electronic device of claim 19 , wherein the first ML model and the second ML model comprise neural networks.

21. The electronic device of claim 18 , wherein sub-circuit performance parameters of the set of sub-circuit performance parameters are determined based on a type of the identified first sub-circuit.