Device settings optimization

A system for optimizing device settings of a plurality of devices, the system comprising:

• a device measurement system configured to receive a performance characteristics target including one or more performance characteristics for the plurality of devices manufactured according to a common design, wherein each of the devices is configurable via a set of device settings and exhibits performance variations over a range of process variations and a range of device operating conditions, the device measurement system further configured to define a plurality of test cases, each test case including a combination of a respective device of the plurality of devices, a process variation within the range of process variations, and a device operating condition within the range of device operating conditions, and to apply a first genetic adaptation algorithm to each of the plurality of test cases to determine, for each test case, a candidate set of device settings that satisfies the performance characteristics target and to produce test results, the test results including the sets of device settings evaluated by the first genetic adaptation algorithm, measured performance characteristics produced by the sets of device settings, and performance-monitoring data;
• a neural network training system configured to train, using the test results, a neural network to generate, for a given set of device settings, predicted performance characteristics;
• and
• a settings generation system configured to determine, by using the trained neural network as a surrogate model of the measured performance characteristics and applying a second genetic adaptation algorithm to evaluate potential sets of device settings using the trained neural network, a global set of device settings that, when applied to the plurality of devices, collectively achieves the performance characteristics target across the plurality of devices under the range of process variations and the range of device operating conditions.

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a performance characteristics target including one or more performance characteristics for a plurality of devices manufactured according to a common design, wherein each of the devices is configurable via a set of device settings and exhibits performance variations over a range of process variations and a range of device operating conditions;
• defining a plurality of test cases, each test case including a combination of a respective device of the plurality of devices, a process variation within the range of process variations, and a device operating condition within the range of device operating conditions;
• applying a first genetic adaptation algorithm to each of the plurality of test cases to determine, for each test case, a candidate set of device settings that satisfies the performance characteristics target and to produce test results, the test results including the sets of device settings evaluated by the first genetic adaptation algorithm, measured performance characteristics produced by the sets of device settings, and performance-monitoring data;
• training, using the test results, a neural network to generate, for a given set of device settings, predicted performance characteristics;
• and
• determining, by using the trained neural network as a surrogate model of the measured performance characteristics and applying a second genetic adaptation algorithm to evaluate potential sets of device settings using the trained neural network, a global set of device settings that, when applied to the plurality of devices, collectively achieves the performance characteristics target across the plurality of devices under the range of process variations and the range of device operating conditions.