Detection and correction of system responses in real-time

1. A method, comprising:

emitting an electron beam towards a specimen;

modulating a beam current of the electron beam to obtain a beam signal;

detecting, using an electron detector, secondary and/or backscattered electrons emitted by the specimen to obtain electron data, wherein the electron data defines a detection signal;

determining, using a processor, a latency between a sine wave controlling the beam signal and a sine wave for the detection signal; and

filtering, using the processor, the detection signal based on the latency.

2. The method of claim 1 , wherein the method further comprises:

applying a high-pass filter to the detection signal to obtain backscattered electron data.

3. The method of claim 2 , wherein filtering the detection signal further includes subtracting the backscattered electron data from the electron data to obtain secondary electron data.

4. The method of claim 2 , wherein filtering the detection signal further includes:

extrapolating, using the processor, a spectral distribution of the backscattered electron data towards a lower energy, thereby yielding an extrapolated backscattered electron data; and

subtracting, using the processor, the extrapolated backscattered electron data from the electron data to obtain secondary electron data.

5. The method of claim 2 , wherein filtering the detection signal further includes:

modelling, using the processor, an energy distribution function of the backscattered electron data, thereby yielding a modeled energy distribution function; and

calibrating, using the processor, the modeled energy distribution function using one or more measurements taken using an electron beam tool.

6. The method of claim 2 , wherein filtering the detection signal further includes determining a relative fraction that is a ratio of an intensity of the backscattered electron data compared to an intensity of the electron data.

7. The method of claim 6 , further comprising storing the relative fraction on an electronic data storage unit.

8. The method of claim 7 , wherein filtering the detection signal further includes using a machine learning model, the machine learning model trained using the stored relative fraction stored on the electronic data storage unit.

9. The method of claim 2 , further comprising:

detecting, using an opposing electron detector, secondary and/or backscattered electrons emitted by the specimen to obtain opposing electron data;

comparing, using the processor, the electron data and the opposing electron data to generate tilt data; and

changing a tilt of the electron beam using the tilt data.

10. A system, comprising:

an electron beam tool including:

an electron beam emitter configured to emit electrons in an electron beam towards a specimen;

a stage configured to hold the specimen;

an electron detector configured to detect secondary and/or backscattered electrons emitted by the specimen to obtain electron data, wherein the electron data defines a detection signal; and

a controller in electronic communication with the electron beam tool, the controller having a processor;

wherein the controller is configured to:

instruct the electron beam emitter to modulate a beam current of the electron beam to obtain a beam signal;

determine a latency between a sine wave controlling the beam signal and a sine wave for the detection signal; and

filter the detection signal using the latency.

11. The system of claim 10 , wherein the controller is further configured to:

apply a high-pass filter to the detection signal to obtain backscattered electron data.

12. The system of claim 11 , wherein the controller is configured to filter the detection signal further by subtracting the backscattered electron data from the electron data to obtain secondary electron data.

13. The system of claim 11 , wherein the controller is configured to filter the detection signal further by:

extrapolating a spectral distribution of the backscattered electron data towards a lower energy, thereby yielding an extrapolated backscattered electron data; and

subtracting the extrapolated backscattered electron data from the electron data to obtain secondary electron data.

14. The system of claim 11 , wherein the controller is configured to filter the detection signal further by:

modelling an energy distribution function of the backscattered electron data, thereby yielding a modeled energy distribution function; and

calibrating the modeled energy distribution function using one or more measurements taken using the electron beam tool.

15. The system of claim 11 , wherein the controller is configured to filter the detection signal further by:

determining a relative fraction that is a ratio of an intensity of the backscattered electron data compared to an intensity of the electron data.

16. The system of claim 15 , further comprising an electronic data storage unit configured to store the relative fraction.

17. The system of claim 16 , wherein the controller is configured to filter the detection signal further using a machine learning model, the machine learning model trained using the stored relative fraction stored on the electronic data storage unit.

18. The system of claim 11 , further comprising:

an opposing electron detector configured to detect secondary and/or backscattered electrons emitted by the specimen to obtain opposing electron data; and

wherein the controller is further configured to:

compare the electron data and the opposing electron data to generate tilt data; and

change an angle of the electron beam emitter using the tilt data, thereby changing a tilt of the electron beam.

19. A non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices:

instructing an electron beam tool to emit an electron beam towards a specimen;

modulating a beam current of the electron beam to obtain a beam signal;

receiving electron data from an electron detector detecting secondary and/or backscattered electrons emitted by the specimen, wherein the electron data defines a detection signal;

determining a latency between a sine wave controlling the beam signal and a sine wave for the detection signal; and

filtering the detection signal based on the latency to obtain a filtered detection signal.

20. The non-transitory computer-readable storage medium of claim 19 , wherein the one or more programs are further configured to execute, on one or more computing devices:

training a machine-learning algorithm using the beam current, the electron data, and the filtered detection signal.