PREDICTING CIRCUIT RELIABILITY AND YIELD USING NEURAL NETWORKS

• US 20150371134A1
• Filed: 06/10/2015
• Published: 12/24/2015
• Est. Priority Date: 06/19/2014
• Status: Abandoned Application

• Current Assignee: Semiconductor Manufacturing International Corporation
• Original Assignee: Semiconductor Manufacturing International Corporation
• Inventors: CHIEN, WEITING, SII, HOWKING, KANG, SHENG
• Application Number:
  US14/736,231
• Publication Number:
  US 20150371134A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: G06F 30/39 Circuit design at the physical level physical level design for reconfigurable circuits G06F30/347
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A system and method for predicting a product characteristic are provided. The system includes a data acquisition module configured to acquire raw data associated with to-be predicted prediction information, a data conversion module configured to convert the raw data into computable normalized data, and a result prediction module configured to calculate a prediction result based on the normalized data and compare the prediction result with a predetermined standard value. The result prediction module includes a neural network prediction model configured to calculate the prediction result based on the normalized data. The prediction information may include reliability and/or yield to prevent major reliability or yield problems from occurring during manufacturing of semiconductor devices.

Claims

1. A system for product reliability and/or yield prediction, comprising:

a data acquisition module configured to acquire raw data associated with to-be predicted prediction information, the to-be predicted prediction information associated with product reliability and/or yield;

a data conversion module configured to convert the raw data into computable normalized data; and

a result prediction module configured to calculate a prediction result based on the normalized data and compare the prediction result with a predetermined standard value;

wherein the result prediction module comprises a neural network prediction model configured to calculate the prediction result based on the normalized data.

2. The system of claim 1, wherein the neural network prediction model comprises a plurality of parameters determined by:

an experimental range including a range of percentages of training, validation, and test data, and a range of neuron counts;

an experimental design table;

a minimum average error value and a maximum R-squared value.

3. The system of claim 1, wherein the result prediction module further comprises a prediction result judgment unit coupled to the neural network prediction model and configured to:

compare the prediction result with the predetermined standard value to obtain a comparison result; and

make an judgment in response to the comparison result.

4. The system of claim 3, wherein the predetermined standard value comprises a valid standard value and an invalid standard value and the prediction result judgment unit makes:

a normal operation judgment when the prediction result is above the valid standard value;

an abnormal operation judgment when the prediction result is below the invalid standard value;

an analysis judgment when the prediction result is between the invalid standard value and the valid standard value.

5. The system of claim 1, wherein the data acquisition module screens the acquired raw data and sends the screened data to a database for storage.

6. The system of claim 1, wherein the raw data comprises:

inline measurement data;

machine monitoring system data;

processing time and idle time data;

wafer electrical test data.

7. The system of claim 6, wherein the machine monitoring system data comprises power, pressure, thermal head temperature, gas, and the inline measurement data comprises a width of metal wirings, a width of trenches, a thickness of an insulating layer, a diameter of a through hole.

8. The system of claim 1, wherein the data conversion module comprises a data format conversion unit and a data normalization unit, the data normalization unit configured to perform the following expression:

(Value−

Min)/(Max−

Min)

where Value is an actual data value, and Max is a maximum data value and Min is a minimum value used for modeling the neural network prediction model.

9. The system of claim 1, further comprising a model parameter test module configured to:

compare an actual test result with the prediction result;

compare a false positive rate with a default value;

cause the system to output model optimization instructions in the event that the actual test result exceeds the prediction result and the false positive rate exceeds the default value; and

cause the system to operate normally in the event that the actual test result does not exceed the prediction result and the false positive rate does not exceed the default value.

10. A computer-implemented method for predicting product reliability and/or yield, the method comprising:

acquiring raw data associated with to-be predicted prediction information using a data acquisition module, the to-be predicted prediction information comprising the product reliability and/or yield;

converting the raw data into computable normalized data;

calculating a prediction result based on the normalized data using a neural network prediction model; and

comparing the prediction result with a predetermined standard value.

11. The computer-implemented method of claim 10, wherein the neural network prediction model comprises a plurality of parameters determined by the following steps:

setting an experimental range including a range of percentages of training, validation, and test data, and a range of neuron counts;

conducting experiments using an experimental design table to obtain one or more experimental results;

judging an average error value in response to the one or more experimental results;

determining a minimum error value as a parameter for the neural network prediction model.

12. The computer-implemented method of claim 10, wherein comparing the prediction result with the predetermined standard value comprises:

in the event that the prediction result is above a valid standard value, determining that the to-be predicted prediction information is normal;

in the event that the prediction result is below an invalid standard value, determining that the to-be predicted prediction information is abnormal;

in the event that the prediction result is between the valid standard value and the invalid standard value, determining that the to-be predicted prediction information is required to be submitted to an analysis.

13. The computer-implemented method of claim 10, wherein the raw data comprises:

inline measurement data;

machine monitoring system data;

processing time and idle time data;

wafer electrical test data.

14. The computer-implemented method of claim 10, wherein converting the raw data into computable normalized data comprises performing an operation using the following expression:

(Value−

Min)/(Max−

Min)

where Value is an actual data value, and Max is a maximum data value and Min is a minimum value used for modeling the neural network prediction model.

15. The computer-implemented method of claim 10, further comprising:

comparing an actual test result with the prediction result;

comparing a false positive rate with a default value;

in the event that the actual test result exceeds the prediction result and the false positive rate exceeds the default value, outputting instructions for model optimization;

in the event that the actual test result does not exceed the prediction result and the false positive rate does not exceed the default value, operating a manufacturing process normally.