Motorola Corporation

Medtronic, Inc.

Eric C Maass

Jian L Cao

Randal C Schulhauser

Patrick W Kinzie

David Probst

Stephen W Dow

Daniel Hahn

David W Feldbaumer

Bill Marlin

In some examples, an apparatus configured to be worn by a patient for cardiac defibrillation comprises sensing electrodes configured to sense a cardiac signal of the patient, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, a memory configured to store the data, the cardiac signal, and a machine learning algorithm, and processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to probabilistically-determine at least one state of the patient and determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined patient state.

Wearable defibrillation apparatus configured to apply a machine learning algorithm

An electronic device (10) comprises a copper integrated inductor (11) overlying other solid state components (31) of the device (10). Preferably, the copper inductor (11) is formed of plated copper to a thickness of several microns. The preferred integrated inductor (11) provides significant advantage over external inductors of the past with respect to simplicity of manufacture as well as system size and cost. Additionally, the preferred inductor (11) provides improved inductance per area, lower series resistance and higher Q values, relative to inductors of the past.

Method of making an electronic device having an integrated inductor

A method for predicting yields for integrated circuit designs for given specification limits and process variations with respect to transistor parametric variations is based on a stastical analysis starting with response surface modeling techniques that relate desired circuit outcomes as a function of a set of defined independent variables. The response surfaces are converted to discrete C.sub.pk surfaces for all combinations of the independent variables. The C.sub.pk surfaces are next converted to discrete percent yield surfaces for each of the circuit outcomes which then are combined to provide a composite yield surface comprising all desired parametric operating points of the outcomes that may be used to predict the circuit yield.

Yield surface modeling methodology

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Scottsdale, AZ, United States of America

Eric C Maass