Patent Description:
The present invention relates generally to acoustic emission monitoring and more particularly to semiconductor device state classification based thereon.

Various types of acoustic emission monitoring systems are known in the art.

<NPL> describes acoustic events that are related to the failure of transistors. An experimental setup is presented, that was used to make a sample of <NUM> insulated gate bipolar transistors (IGBT) to fail.

<CIT> describes a method of detecting the fault of a semiconductor device by detecting the acoustic emission at the semiconductor device to notify abnormal heat therefrom.

<NPL>, investigates a method to measure fatigue mechanisms in standard module technology of semiconductor power devices. To detect the degradation in power modules, the principle of acoustic emission was used.

<NPL>, presents observations of acoustic emission in power semiconductor components. The acoustic emissions are caused by the switching operation and failure of power transistors. Three types of acoustic emission are observed. Furthermore, aspects related to the measurement and detection of acoustic phenomena are discussed.

Aspects of the invention relate to a system for monitoring and identifying states of a semiconductor device as defined in claim <NUM>, and a method for monitoring and identifying states of a semiconductor device as defined in claim <NUM>. Optional features of embodiments are set out in the dependent claims.

The present disclosure seeks to provide novel systems and methods relating to the monitoring of acoustic emission generated by semiconductor devices and to the classification of operational states of semiconductor devices based on such monitoring.

There is thus provided in accordance with a preferred embodiment of the presently claimed invention a system for monitoring and identifying states of a semiconductor device, the system including at least one acoustic sensor for sensing acoustic emission emitted by at least one semiconductor device operating at a voltage of less than or equal to <NUM> V, the semiconductor device being one of a microcontroller device or a flash memory device comprising floating gate MOSFET transistors, and the at least one acoustic sensor outputting at least one acoustic emission signal and a signal processing unit for receiving the at least one acoustic emission signal from the at least one acoustic sensor and for analyzing the at least one acoustic emission signal, the signal processing unit providing an output based on the analyzing, the output being indicative at least of whether the at least one semiconductor device is in an abnormal operating state with respect to a normal operating state of the semiconductor device. The output is also indicative of whether said semiconductor device is in an active operating state or an idle operating state.

In accordance with a preferred example, the normal operating state includes a healthy state and the abnormal operating state includes a defective state.

In accordance with another preferred example, the normal operating state includes a legitimate state and the abnormal operating state includes an illegitimate state due to malicious interference in at least one of the at least one semiconductor device and at least one additional device cooperating therewith.

Preferably, the analyzing at least includes comparing the acoustic emission signal to at least one predetermined acoustic emission signal.

Preferably, the analyzing at least includes comparing the acoustic emission signal to at least one predetermined acoustic emission threshold.

Preferably, the at least one predetermined acoustic emission signal includes at least one historical acoustic emission signal corresponding to at least one historical operating state of the semiconductor device.

Additionally or alternatively, the at least one predetermined acoustic emission signal includes a collection of historical acoustic emission signals from a plurality of electronic devices having at least one shared electrical characteristic with the semiconductor device, the collection of historical acoustic emission signals corresponding to a collection of historical operating states of the plurality of electronic devices.

Preferably, the comparing includes statistical analysis of correlations between features of the acoustic emission signal and features of the at least one predetermined acoustic emission signal.

Preferably, the analyzing includes machine learning functionality.

Preferably, the machine learning functionality is operative at least to extract features from a training set of historical acoustic emission signals from at least one additional electronic device having at least one shared electrical characteristic with the semiconductor device, the features extracted from the training set being associated with corresponding operational states of the at least one additional electronic device, and to identify the operating state of the semiconductor device based on correlations between the features extracted from the training set and features of the acoustic emission signal.

Preferably, the training set of historical acoustic emission signals does not include historical acoustic emission signals from the semiconductor device.

Preferably, the system also includes at least one additional sensor measuring at least one additional parameter associated with the at least one semiconductor device, the analyzing including comparing the at least one additional parameter to the acoustic emission signal.

Preferably, the at least one additional sensor includes an antenna and the at least one additional parameter includes electromagnetic radiation.

Preferably, the system also includes an alert module, the alert module being operative to provide the output to a user in at least near real time.

Preferably, the system also includes a control unit communicatively coupled to the signal processing unit, the control unit providing automatic feedback control to the at least one semiconductor device, based on the output.

Preferably, the control unit includes functionality for scheduling at least one repair related operation on the semiconductor device based on the output to the user.

Preferably, the least one semiconductor device includes a field effect transistor.

Preferably, the least one semiconductor device operates at a power of less than or equal to <NUM> kW.

Preferably, the at least one semiconductor device operates with a current of less than or equal to 1A.

Preferably, the at least one semiconductor device includes an integrated circuit.

There is also provided in accordance with another preferred embodiment of the presently claimed invention a method for monitoring and identifying states of a semiconductor device, the method including sensing acoustic emission emitted by at least one semiconductor device operating at a voltage of less than or equal to <NUM> V, the semiconductor device being one of a microcontroller device or a flash memory device comprising floating gate MOSFET transistors, analyzing the acoustic emission and providing an output based on the analyzing, the output being indicative at least of whether the at least one semiconductor device is in an abnormal operating state with respect to a normal operating state of the semiconductor device. The output is also indicative of whether said semiconductor device is in an active operating state or an idle operating state.

Preferably, the analyzing at least includes comparing the acoustic emission to at least one predetermined acoustic emission signal.

Preferably, the analyzing at least includes comparing the acoustic emission to at least one predetermined acoustic emission threshold.

Preferably, the at least one predetermined acoustic emission signal includes a collection of historical acoustic emission signals from a plurality of electronic devices having at least one shared electrical characteristic with the semiconductor device, the collection of historical acoustic emission signals corresponding to a collection of historical operating states of the plurality of electronic devices.

Preferably, the comparing includes statistical analysis of correlations between features of the acoustic emission and features of the at least one predetermined acoustic emission signal.

Preferably, the machine learning functionality is operative at least to extract features from a training set of historical acoustic emission signals from at least one additional electronic device having at least one shared electrical characteristic with the semiconductor device, the features extracted from the training set being associated with corresponding operational states of the at least one additional electronic device, and to identify the operating state based on correlations between the extracted features and features of the acoustic emission.

Preferably, the method also includes measuring at least one additional parameter associated with the at least one semiconductor device, the analyzing including comparing measurements of the at least one additional parameter to the acoustic emission.

Preferably, the at least one additional parameter includes electromagnetic radiation.

Preferably, the method also includes providing the output to a user in at least near real time.

Preferably, the method also includes providing automatic feedback control to the at least one semiconductor device, based on the output.

Preferably, the method also includes scheduling at least one repair related operation on the semiconductor device based on the output to the user.

Preferably, the at least one semiconductor device includes a field effect transistor.

Preferably, the at least one semiconductor device operates at a power of less than or equal to <NUM> kW.

The present invention will be understood and appreciated more fully based on the following detailed description taken in conjunction with the drawings in which:.

Reference is now made to <FIG> which is a simplified block-diagram illustration showing components of an acoustic emission monitoring system constructed and operative in accordance with a preferred embodiment of the present invention.

As seen in <FIG>, there is provided an acoustic emission monitoring system <NUM> for measurement of acoustic emission signals emitted by semiconductor devices and for the identification of operational states of the monitored semiconductor devices, including identification of possible defects in the semiconductor device, based thereon.

Acoustic monitoring system <NUM> is preferably operative to monitor the acoustic emissions generated and emitted by at least one semiconductor device, here embodied, by way of example, as a single semiconductor device <NUM>. It is appreciated, however, that the depiction of a single semiconductor device in <FIG> is for simplicity of representation only and that system <NUM> may be adapted to monitor multiple semiconductor devices. Semiconductor device <NUM> may be one or more semiconductor component typically but not necessarily mounted on a printed circuit board (PCB), including transistors, microprocessors, central control units, memory devices, field effect transistors and other semiconductor components as are well known in the art. Semiconductor device <NUM> may alternatively be any electronic component comprising an integrated circuit (IC), which IC may be mounted on a PCB or may be a bare chip.

Semiconductor device <NUM> operates at a voltage of less than or equal to <NUM> V and preferably a power of less than or equal to <NUM> kW. Semiconductor device <NUM> may operate at a current in the range of 1mA - 1A. It is appreciated that semiconductor device <NUM> is thus preferably a relatively low power device. In some embodiments of the present invention, semiconductor device may be a microelectronic device. Semiconductor device <NUM> may also function in cooperation with, for example as controllers of, higher power electronics.

The use of a system such as acoustic monitoring system <NUM> to monitor semiconductor devices is based on the phenomenon of the generation of measurable acoustic emission signals <NUM> by such devices. These acoustic emission signals <NUM> have been found to arise from the semiconductor device itself, such that the semiconductor device is the source of the acoustic emission and the acoustic emission is originally created thereby.

In contrast to conventional acoustic monitoring systems, typically measuring acoustic emissions arising from mechanical faults in mechanical systems or acoustic emissions due to electrical breakdown of and/or electrical discharge by power electronics, the present invention utilizes the measurable acoustic emission signals found to be generated by relatively low-power semiconductor devices.

The creation of acoustic emission signals by the relatively low-power semiconductor devices in accordance with the present invention is believed by the present inventors to be due to electrical current causing charge-lattice interactions as a result of scattering of charges or due to electromagnetic forces, leading to atomic motion and hence the generation of measurable acoustic emission. For example, in the case of a floating gate MOSFET transistor, the tunneling of electrons into the floating gate is accompanied by charge-lattice interactions, which charge-lattice interactions are believed to be a source of acoustic emission.

These measurable acoustic emission signals have been found by the present inventors to be characterized by various characteristics in the time and/or frequency domain, depending on the operating state of the device.

Furthermore, the present inventors have found the acoustic emission signals to be influenced by electrical, thermal and/or mechanical stresses, including electrical failure, to which the semiconductor device may be subject. As a result, changes in the acoustic emission signals of the semiconductor device may be used to identify the presence of influential thermal, mechanical and or electrical stresses, which stresses may cause defects in the monitored device.

The measured acoustic emission signals thus may be used to identify a state of the monitored device, such as on or off, identify internal device processes, such as read or write operation in a flash memory or the working mode of a micro-CPU, and to predict and/or diagnose malfunctions and deterioration of the device, as is further detailed henceforth. Such diagnosis may be valuable for monitoring and defect detection in expensive and/or mission critical electronic equipment and/or computational semiconductor devices.

Additionally, changes in the acoustic emission signals generated by an semiconductor device may be used to identify possible security breaches in control of the device, due for example to hacking or other malicious activities directed against the device via computerized controls thereof.

The acoustic emission <NUM> emitted by semiconductor device <NUM> is preferably sensed by at least one acoustic measurement module, here embodied, by way of example, as an acoustic emission sensor <NUM>. Acoustic emission sensor <NUM> preferably directly senses emission from device <NUM>, which emission is generated by device <NUM> itself. Acoustic emission sensor <NUM> may be any type of acoustic emission sensor suitable for measuring acoustic emission, types of which are well known in the art and various examples of which are provided henceforth. Particularly, acoustic emission sensor <NUM> may be one or more physically contacting or non-contacting sensor. The use of multiple acoustic emission sensors may be advantageous by enabling cross-correlation between the measured signals to allow cancelling out of environmental noise. In one possible embodiment of the present invention, acoustic emission sensor <NUM> may be incorporated within semiconductor device <NUM>, for example, as a component installed on the PCB of device <NUM>.

At least one acoustic emission sensor <NUM> is preferably operative to sense acoustic emission emitted by at least one semiconductor device <NUM> and to output at least one acoustic emission signal corresponding thereto. Acoustic emission sensor <NUM> is preferably operative to sense acoustic emissions over at least one acoustic frequency range, which frequency range may comprise ultrasonic and/or sonic frequencies from several Hz to the GHz range.

System <NUM> may optionally also include at least one additional sensor <NUM> for sensing at least one other parameter associated with semiconductor device <NUM>, in addition to the sensing of acoustic emission by acoustic emission sensor <NUM>. By way of example, additional sensor <NUM> may sense one or more of electromagnetic emission, temperature, magnetic field strength and direction of device <NUM>.

System <NUM> further includes a signal processing unit <NUM> for receiving the at least one acoustic emission signal output by acoustic emission sensor <NUM> and analyzing the measured acoustic emission. Signal processing unit <NUM> provides an output <NUM> based on results of the analyzing performed thereby, which output is indicative at least of an operational state of device <NUM>.

The signal processing unit provides an output indicative at least of whether at least one semiconductor device <NUM> is in an abnormal operating state with respect to a normal operating state of the semiconductor device <NUM>. A normal operating state may correspond to a healthy operating state of semiconductor device <NUM> and an abnormal operating state may correspond to a defective state of semiconductor device <NUM>. A normal operating state may additionally or alternatively correspond to a legitimate state of semiconductor device <NUM> and an abnormal operating state may correspond to an illegitimate state of semiconductor device <NUM>, due to malicious interference in the controls of the semiconductor device <NUM> or in the controls of an additional device cooperating with semiconductor device <NUM>. Further by way of example, the output <NUM> may be indicative of device <NUM> being in a properly functioning state or in a state of potential or impending malfunction. Additionally , the output is indicative of device <NUM> being active or idle, as is further detailed henceforth.

Detection of an abnormal operating state of the device may have a variety of practical applications, including, by way of example only, prediction of failure of the device based on the device defect, detection of performance degradation of the device, detection of security breaches in the control of the device or detection of errors in computer code controlling the device.

In accordance with one preferred embodiment of the present invention, the analyzing performed by signal processing unit <NUM> may include comparing the measured acoustic emission signal to at least one predetermined acoustic emission signal. Such comparing may be carried out in the time and/or frequency domain. Upon detection of deviation of the measured acoustic emission signal from the predetermined acoustic emission signal, signal processing unit <NUM> may provide an output indicative of such deviation.

The predetermined acoustic emission signal and corresponding deviation therefrom may be one or more of an experimentally determined threshold signal associated with a given device, exceedance of which is indicative of a defect or of potential malfunction of the device; a historical emission signal or set of signals associated with a given device, deviation from which by a given statistical measure is indicative of a defect or potential malfunction of the device; and a collection of historical emission signals or set of signals from corresponding although not necessarily identical electronic devices, which electronic devices may be semiconductor devices, deviation from which by a given statistical measure is indicative of a defect or potential malfunction of the device. Such corresponding devices may share at least one common electrical feature with monitored device <NUM>.

Additionally or alternatively, signal processing unit <NUM> may include machine learning functionality. Machine learning functionality may be particularly useful in identifying when semiconductor device <NUM> or additional devices cooperating with semiconductor device <NUM> are affected by hacking or other malicious activities. In the case that semiconductor device <NUM> or additional devices cooperating with semiconductor device <NUM> is subject to a malicious attack, the acoustic emission may deviate with respect to baseline acoustic emission patterns established during regular non-interfered operation of semiconductor device <NUM> or of other similar corresponding semiconductor devices sharing at least one electrical characteristic with semiconductor device <NUM>. Machine learning algorithms as described henceforth may be used to detect such deviations and identify security breaches based thereon.

Machine learning functionality included in signal processing unit <NUM> may be operative, by way of example, to perform machine learning on features of historical training data, the features being associated with corresponding operational states of devices from which the historical training data was obtained, and hence to classify an operating state of semiconductor device <NUM> based on correlations between features of the historical data and features of the measured acoustic emission signal of semiconductor device <NUM>. The machine learning functionality of signal processing unit <NUM> may include any type of machine learning-based data mapping, processing and classification, including, by way of example only, statistic classifiers and self-learning neural networks. Further details concerning the various possible modes of operation of signal processing unit <NUM> are provided with reference to <FIG>.

Irrespective of the particular type of signal processing implemented by signal processing unit <NUM>, analysis of the measured acoustic emission may be used to classify an operating state of semiconductor device <NUM>. Particularly, analyzed features of the measured acoustic emission may be indicative of the electronic device being subject to stresses and/or failures, including electrical, thermal, and mechanical stresses, and hence be useful in diagnosis of defects and prognosis of impending malfunction of the device.

In the case that additional sensors <NUM> are included in system <NUM>, signal processing unit <NUM> preferably receives an output from additional sensors <NUM>, which output is indicative of those parameters sensed by sensors <NUM>. Signal processing unit <NUM> may compare the output from additional sensors <NUM> to the measured acoustic emission signal provided by acoustic emission sensor <NUM>, in order to derive possible correlations therebetween.

The output <NUM> of signal processing unit <NUM> may be received by an alert unit <NUM>, for alerting a user in at least near real time of the operating state of semiconductor device <NUM>. By way of example, alert unit <NUM> may provide a human sensible output alarm indication including at least a prediction of failure or time to failure of device <NUM> where relevant.

Furthermore, alert unit <NUM> may be communicatively coupled to a feedback control unit <NUM>, operative to provide automatic feedback control to semiconductor device <NUM>, based on the nature of the operating state identified, including the potential malfunction or defect detected. By way of example, upon identification of potential malfunction, feedback control unit <NUM> may modify or switch off the power supply to device <NUM>. Additionally or alternatively, feedback control unit <NUM> may incorporate functionality for scheduling at least one repair or maintenance related operation on the device <NUM> based on the human sensible output alarm indication provided by alert unit <NUM>.

It is appreciated that whereas acoustic emission sensor <NUM> and additional optional sensors <NUM> are preferably included in a dedicated device package located proximal to device <NUM> being monitored, this is not necessarily the case for other components of system <NUM>. By way of example, one or more of signal processing unit <NUM>, alert unit <NUM> and feedback control unit <NUM> may be included in a device package or may be part of a cloud service.

Furthermore, it is appreciated that the functionalities of signal processing unit <NUM>, alert unit <NUM> and feedback control unit <NUM> may be merged or redistributed according to particular system requirements. For example, signal processing unit <NUM> may include the functionality of alert unit <NUM>, alert unit <NUM> may include the functionality of feedback control unit <NUM> or acoustic emission sensor <NUM> may be combined with additional sensor modules <NUM>. It is further appreciated that the inclusion of feedback control unit <NUM> in system <NUM> is optional only, and that feedback control unit <NUM> may be obviated, for example in the case of a user of device <NUM> making manual rather than using automatic adjustments thereto upon receiving an alert from alert unit <NUM>.

Reference is now made to <FIG>, which is a simplified flow chart illustrating signal acquisition and processing functionality of a system of the type shown in <FIG>. Particularly preferably, <FIG> illustrates signal processing functionality of a signal processing unit of system <NUM>, such as signal processing unit <NUM>.

As seen in <FIG>, the signal processing functionality includes steps for calibration of a given semiconductor device under test (DUT), as illustrated in a first calibration column <NUM>, as well as steps for actual measurement of the DUT, as illustrated in a second measurement column <NUM>.

Turning now to first calibration column <NUM>, the DUT is preferably calibrated at a first calibration step <NUM>. First calibration step <NUM> preferably involves the measurement of acoustic emission and calibration thereof in a variety of operational states of the DUT, including, by way of example, one or more of a non-powered state, an idle state, an active state, an electrically stressed state, a mechanically stressed state and a heated or cooled state. Such calibration may be used to establish a baseline acoustic emission signal, corresponding to normal operation of the DUT, which normal operation may be healthy rather than faulty operation or legitimate rather than illegitimate operation.

First calibration step <NUM> may involve the measurement and calibration of acoustic emissions from the DUT itself, or from a population of similar electronic devices resembling but not necessarily identical to the DUT, using a crowd-sourcing approach. By way of example, members of a population of electronic devices used for calibration measurements may be selected based on having at least one electrical characteristic in common with the DUT such as, by way of example, semiconductor devices having similar power consumption in similar operational states. The population of electronic devices based on which a given DUT may be calibrated may or may not include the DUT itself.

The calibrated output for the various operational states of the DUT is preferably used to establish emission patterns or features associated with various DUT conditions, as seen at a second calculation step <NUM>. Such emission features may be thresholds based on one or both of time domain and frequency domain spectral features of acoustic emission of the DUT in the various calibrated operational states thereof. Such emission features may additionally or alternatively be machine-learning based data trends or models. These emission features may be used to build up a dictionary of data features, as seen at third compilation step <NUM>.

By way of example, the emission features derived at second calculation step <NUM> may be discrete acoustic signal thresholds corresponding to respective operational states of the DUT. These discrete thresholds may be unique to the particular DUT or may be standard thresholds found to be applicable to a range of similar semiconductor devices.

Alternatively, the emission features derived at second step <NUM> may correspond to models of acoustic emission signals statistically correlated to respective operational states of the DUT, which models may be based on historical measurements of the acoustic emission signal over time and between various operating conditions of the DUT.

Additionally or alternatively, the dictionary compiled at third step <NUM> may comprise or be augmented by data patterns identified based on statistical models of acoustic emission signals gleaned from historical measurements of acoustic emission signals of electronic devices sharing electrical characteristics with the DUT but not necessarily being identical thereto, based on a crowd-sourcing approach. The incorporation of data patterns based on related electronic devices in the emission patterns dictionary at third step <NUM> allows the compilation of a richer, more widely applicable dictionary having a higher confidence level associated therewith.

It is appreciated that first - third steps <NUM> - <NUM> shown in calibration column <NUM> are not necessarily carried out by signal processing unit <NUM>. Depending on the particular thresholds applied, first - third steps <NUM> - <NUM> may be carried out by external, additional signal collection and processing modules and the emission pattern dictionary compiled at third step <NUM> stored at signal processing unit <NUM> or at a server.

Turning now to second measurement column <NUM>, acoustic emission data generated by the DUT is received at a fourth step <NUM>. By way of example, acoustic emission data may be acquired by acoustic emission sensor <NUM> and received therefrom by signal processing unit <NUM>.

At a fifth step <NUM>, data features are extracted from the received data. Feature extraction may include extraction of physical features of the acoustic emission, such as total acoustic emission energy, acoustic energy within defined time frames, acoustic energy within defined frequency bins and fluctuations in acoustic energy. Feature extraction may also include extraction of statistical features of the acoustic emission, including statistical moments and correlations and cumulants of acoustic signal, signal entropy and signal noise, as well as extraction of signal integrity features such as signal span and stationarity.

At a sixth step <NUM> and seventh step <NUM>, features extracted at fifth step <NUM> are respectively validated by and compared to features of data patterns held in the dictionary built up at third step <NUM>. Particularly, features extracted from the received data may be compared to features of the baseline acoustic emission signal, such that validation of the features takes into account the baseline acoustic emission associated with normal operation of the DUT. Validated features may be fed back to the dictionary, thereby further building up the DUT dictionary. As a result of such feedback, the reference data patterns held in the DUT dictionary may be dynamically changing patterns. Feature validation may include comparing patterns of change over time of the acoustic emission signal sensed from the DUT to patterns of change over time of historical acoustic emission signals associated with past failures of the DUT or of electronic devices similar to the DUT.

Extracted features may be within predefined or machine-learned limits, allowing classification of the state of the DUT, as seen at an eighth step <NUM>, leading to generation of a device status at a ninth step <NUM>. The status may indicate deterioration of the DUT and predict impending failure prior to the occurrence of operational failure. Furthermore, the status may indicate the particular nature of the operational failure likely to occur. Alternatively, extracted features may deviate from the pre-defined or machine-learned baseline signals, indicating anomalous operation of the DUT as seen at a tenth step <NUM>. Identification of malfunction of the DUT may result in the generation of a malfunction alert and/or feedback to the DUT, for example by way of alert unit <NUM> and feedback control unit <NUM> respectively.

For example, in the case that patterns of change over time of the acoustic emission signal sensed from the DUT are found to be similar to patterns of change over time of historical acoustic emission signals associated with past failures of the DUT or of electronic devices similar to the DUT, an output may be generated by alert unit <NUM> comprising a prediction of impending failure of the DUT based on similarities between patterns of change over time of the present measured acoustic emission signal and patterns of change over time of historical acoustic emission signals.

Extracted features found to deviate from the pre-defined or machine-learned limits may also be fed back to the data feature dictionary in order to update the data feature dictionary.

By way of example, in the case that a system such as system <NUM> is used in detecting anomalous operating states as means of identifying undesirable malicious interference in the operation of a monitored electronic device, the signal processing unit may receive measured acoustic emission signals and extract features therefrom. The signal processing unit may furthermore identify at least one operating state of the semiconductor device <NUM> based on the extracted features and compare the least one identified operating state to historical operating states of at least one reference electronic device having at least one shared electrical characteristic with the monitored semiconductor device. It is appreciated that the historical operating states may or may not include historical operating states of the monitored semiconductor device itself.

Additionally, the signal processing unit may provide an output based on the comparing, the output being indicative at least of whether the identified operating state is anomalous with respect to the historical operating states. As detailed above, an anomalous operating state may be caused, for example, by security breaches in the operation of the device or errors in code operating the device.

In the case that feature extraction and validation involves machine learning, a possible input of machine learning algorithms is a normalized set of various feature parameters as described above and the desired output may be, for example, predicted time-to-failure of the DUT. Training of such machine learning algorithms is preferably performed by providing historical examples of data relating to failures and faults. During an evaluation stage, each time data is recorded from the acoustic emission sensors relevant parameters are calculated on the data, which parameters may be identified as p1, p2 etc, as indicated in equation (<NUM>) below.

These parameters may include, for example, peak amplitude, peak frequency, time waveform and total energy. The data may then be normalized using Z-score transformation relative to a historical baseline, in accordance with equation (<NUM>) below. <MAT> where <MAT> and µi is mean of parameter pi under similar operating conditions in the same or similar device. In a more general multivariate case: <MAT> where µ is a mean of parameter vector p known from historical data, and Σ is a covariance matrix calculated from historical data as well. The output of the system is expected time-to-failure (Tttf).

During a training stage, various parameters are calculated using historical data as the input to the algorithm and time-to-failure provided as a target output. In this formulation the task is a simple regression: <MAT> where C represents parameters of the learning system calculated from historical data on the same or similar devices. One of the simplest solutions is using linear or logistic regression. In a linear case: <MAT>.

It is understood that the forgoing corresponds to one possible implementation of machine learning algorithms useful in the present invention, and that the use of any appropriate machine learning algorithm may be possible.

It is appreciated that the signal processing steps illustrated in <FIG> are not necessarily carried out in the order shown and described and that various steps may be interchanged with other steps. Furthermore, it is appreciated that the signal processing steps may include additional steps not described herein, as may be known in the art.

Reference is now made to <FIG>, which is a partially pictorial, partially block-diagram illustration of an implementation of a system for monitoring acoustic emission, constructed and operative in accordance with a preferred embodiment of the present invention.

As seen in <FIG>, an electrical motor <NUM> is controlled by a controller <NUM>. Controller <NUM> may comprise a PCB having mounted thereon at least one semiconductor device <NUM> such as, by way of example only, a micro-CPU drawing a current of several mA. Acoustic emission generated by micro-CPU <NUM> may be monitored by an acoustic emission sensor module <NUM>. Acoustic emission sensing module <NUM> preferably comprises at least one acoustic emission sensor for sensing acoustic emission generated by micro-CPU <NUM>. Acoustic emission sensing module <NUM> may be directly mounted on micro-CPU <NUM>, such that the at least one acoustic emission sensor is physically contacting the micro-CPU. Alternatively, acoustic emission sensing module <NUM> may be spatially separated from the micro-CPU <NUM>, such that the acoustic emission sensor is not in physical contact therewith. It is appreciated that micro-CPU <NUM> and acoustic emission sensing module <NUM> correspond to a preferred embodiment of semiconductor device <NUM> and acoustic emission sensor <NUM> of <FIG>.

Acoustic emission signals generated by micro-CPU <NUM> are preferably but not necessarily continuously sensed by acoustic emission sensing module <NUM>. Acoustic emission sensing module <NUM> preferably outputs acoustic emission signals <NUM> corresponding to the acoustic emission spectra generated by micro-CPU <NUM>. Acoustic emission signals <NUM> are preferably but not necessarily output by acoustic emission sensing module in real time or near real time.

Acoustic emission signals <NUM> output by acoustic emission sensing module <NUM> are preferably provided to a signal processing subsystem <NUM> forming a part of an acoustic emission monitoring system <NUM>. The analyzing performed by signal processing subsystem <NUM> may include application of an algorithm for extracting features of acoustic emission signals <NUM>, in accordance with the various functionalities described hereinabove with reference to <FIG>.

A set of signal features <NUM> extracted by signal processing subsystem <NUM> may be provided by signal processing subsystem <NUM> to a server <NUM>, typically on the cloud, for further processing. At the server <NUM>, extracted features <NUM> may be compared to signal features stored in a cloud-based data feature dictionary <NUM>. Particularly, extracted features <NUM> may be compared to features held in dictionary <NUM> and associated with normal operation of micro-CPU <NUM> in order to detect whether features associated with the present operation of micro-CPU <NUM> are within predefined or machine-learned limits corresponding to normal operation of micro-CPU <NUM>.

Data feature dictionary <NUM> may include predefined thresholds or data patterns derived based on past operation of micro-CPU <NUM>. Additionally or alternatively, data feature dictionary <NUM> may be dynamically compiled based on crowd-sourcing of acoustic emission data patterns from a population of electronic components sharing electronic characteristics with micro-CPU <NUM>.

Based on a comparison of features <NUM> to features held in data feature dictionary <NUM>, server <NUM> may output a classification <NUM> of a state of micro-CPU <NUM>. Classification <NUM> of the state of micro-CPU <NUM> is preferably provided to a notification sub-system <NUM>. Notification sub-system <NUM> preferably forms a part of acoustic emission monitoring system <NUM>.

In the case that features <NUM> are found to deviate from features included in data feature dictionary <NUM> by machine-learned or predetermined statistical limits, notification sub-system <NUM> preferably outputs a human-sensible alert <NUM> indicating a state of micro-CPU <NUM>. In the exemplary embodiment illustrated in <FIG>, notification sub-system <NUM> preferably outputs a human-sensible alert <NUM> in the form of a message, stating that unusual acoustic emission spectra have been detected from micro-CPU <NUM>. Acoustic emission monitoring system <NUM> may be executed by a computer <NUM> used by a user <NUM> to whom alert <NUM> is provided.

Such unusual acoustic emission spectra may indicate a possible security breach in the control and/or operation of motor <NUM> cooperating with and controlled by controller <NUM>, of which controller <NUM> micro-CPU <NUM> forms a part. The acoustic emission signature of micro-CPU <NUM> thus may be used to evaluate whether micro-CPU <NUM> is operating in accordance with normal operating patterns or abnormal operating patterns, which abnormal operating patterns may be due to malicious interference in the operation of the motor <NUM> to which the micro-CPU <NUM> is connected.

Reference is now made to <FIG>, which is a partially pictorial, partially block-diagram illustration of an implementation of a system for monitoring acoustic emission, constructed and operative in accordance with another preferred embodiment of the present invention.

As seen in <FIG>, an aircraft <NUM> may include electronic circuitry <NUM>. Electronic circuitry <NUM> may include a variety of semiconductor components <NUM>, including low-power semiconductor components, integrated circuits and PCBs on which a plurality of semiconductor components are mounted. By way of example, semiconductor components <NUM> may be one or more of a CPU, microcontroller or memory chip or may be one or more PCBs on which one or more of such semiconductor components are mounted. It is appreciated that electronic circuitry <NUM> is illustrated herein in a highly simplified form and may include a far greater and more complex arrangement of electronic components and PCBs therefore, as is well known in the art. Semiconductor components <NUM> may be operative to provide power, control or other functionalities to aircraft <NUM>.

An acoustic emission sensing module <NUM> is preferably coupled to at least one of semiconductor components <NUM>. Acoustic emission sensing module <NUM> preferably comprises at least one acoustic emission sensor for sensing acoustic emission generated by at least one semiconductor component <NUM>. Acoustic emission sensing module <NUM> may be directly mounted on semiconductor component <NUM>, such that the at least one acoustic emission sensor is physically contacting the semiconductor component <NUM>. Additionally or alternatively, acoustic emission sensing module <NUM> may be spatially separated from the semiconductor component <NUM>, such that the acoustic emission sensor is not in physical contact therewith. It is appreciated that semiconductor component <NUM> and acoustic emission sensing module <NUM> correspond to a preferred embodiment of semiconductor device <NUM> and acoustic emission sensor <NUM> of <FIG>.

Acoustic emission generated by semiconductor component <NUM> are preferably but not necessarily continuously sensed by acoustic emission sensing module <NUM>. Acoustic emission sensing module <NUM> preferably outputs acoustic emission signals <NUM> corresponding to the acoustic emission spectra generated by electronic component <NUM>. Acoustic emission signals <NUM> are preferably but not necessarily output by acoustic emission sensing module <NUM> in real time or near real time.

Acoustic emission signals <NUM> output by acoustic emission sensing module <NUM> are preferably provided to a signal processing subsystem <NUM> forming a part of an acoustic emission monitoring system <NUM>. Signal processing subsystem <NUM> may comprise computing functionality for analyzing the acoustic emission signals <NUM>. The analyzing performed by signal processing subsystem <NUM> may include application of an algorithm for extracting features of acoustic emission signals <NUM>, in accordance with the various functionalities described hereinabove with reference to <FIG>.

Acoustic emission monitoring system <NUM> may optionally include other sensors, in addition to acoustic emission sensing module <NUM>, in order to sense other parameters associated with electronic circuitry <NUM>. By way of example, other sensed parameters associated with electronic circuitry <NUM> may include temperature, electromagnetic radiation, magnetic field strength and humidity. In the case that such parameters are sensed by acoustic emission monitoring system <NUM>, an additional signal output representative of such parameters is preferably provided to signal processing subsystem <NUM> in addition to acoustic emission signals <NUM>. Signal processing subsystem <NUM> is preferably operative to extract features of such additional signals.

A set of signal features <NUM> preferably extracted by signal processing subsystem <NUM> may be provided by signal processing subsystem <NUM> to a server <NUM>, typically on the cloud, for further processing. At the server <NUM>, extracted features <NUM> may be compared to signal features stored in a cloud-based data feature dictionary <NUM>. Extracted features <NUM> may be compared to features held in dictionary <NUM> and associated with regular, healthy operation of semiconductor component <NUM> in order to detect whether features associated with the present operation of semiconductor component <NUM> are within predefined or machine-learned limits corresponding to normal operation thereof. Furthermore, extracted features <NUM> may be compared to features held in dictionary <NUM> and associated with malfunction or impending failure of semiconductor component <NUM>, in order to detect whether features associated with the present operation of semiconductor component <NUM> are indicative of incipient failure thereof.

In the case that system <NUM> includes additional parameter sensors for sensing additional parameters associated with electronic component <NUM>, extracted features of such additional parameter signals may also be analyzed at server <NUM> in order to ascertain possible correlations between features of the acoustic emission signals <NUM> and features of the additional signals representing parameters other than acoustic emission.

Data feature dictionary <NUM> may include predefined thresholds or data patterns derived based on past operation of circuitry <NUM> and/or electronic component <NUM>. Additionally or alternatively, data feature dictionary <NUM> may be dynamically compiled based on crowd-sourcing of acoustic emission data patterns from a population of electronic circuits or electronic components sharing at least one electronic characteristic with circuitry <NUM> and/or semiconductor component <NUM>.

Based on a comparison of features <NUM> to data feature dictionary <NUM>, server <NUM> may output a classification <NUM> of a state of electronic circuitry <NUM> and/or electronic component <NUM>. Classification <NUM> is preferably provided to a notification sub-system <NUM>. In the case that features <NUM> are found to deviate from features included in data feature dictionary <NUM> by machine-learned or predetermined statistical limits, notification sub-system <NUM> may output a human-sensible alert indicating such deviation.

Classification <NUM> may include a classification of the device status as healthy, malfunctioning or of incipient failure of the device. Furthermore, classification <NUM> may include an indication of a particular fault or nature of a particular impending failure of circuitry <NUM> and/or semiconductor component <NUM>.

Classification <NUM> may be provided to a control subsystem <NUM>, which control subsystem <NUM> is preferably in controlling communication with electronic circuitry <NUM> and/or semiconductor component <NUM>. Control subsystem <NUM> is preferably operative to provide a feedback control <NUM> to electronic circuitry <NUM> and/or semiconductor component <NUM> responsive to the content of classification <NUM>. Feedback control <NUM> is preferably, but not necessarily, automatic.

By way of example, in the case that classification <NUM> includes an indication of malfunction of semiconductor component <NUM>, feedback control <NUM> may include control instructions to power down circuitry <NUM> and/or semiconductor component <NUM>. Alternatively, in the case that classification <NUM> includes an indication of incipient failure of semiconductor component <NUM>, feedback control <NUM> may include control instructions to change the operating state of circuitry <NUM> and/or semiconductor component <NUM>. Such change of state may include, by way of example, putting the semiconductor component into an idle rather than active operating mode or modifying the power supply to the semiconductor component.

It is appreciated that acoustic emission monitoring system <NUM> thus provides advance identification of malfunction and detection of faults in monitored semiconductor components, thus allowing pre-emptive control changes to the monitored components. Such pre-emptive control changes may mitigate or obviate damage that would arise from the failure of such components, should no such control changes be made, including failure of critical electrical systems and consequent risk to human life.

It is understood that the use of an acoustic emission monitoring system such as system <NUM> is not limited to the acoustic emission monitoring and control of low power semiconductor circuits and ICs in aircraft, and may be implemented in any other application including low-power electronic components, such as car circuits and military equipment.

The creation and generation of measurable acoustic emission signals by semiconductor devices, classification of the semiconductor device state based on characteristics of the measured acoustic emission signals and detection of potential malfunction of the device due to the influence of electrical, mechanical, and/or thermal stresses on the measured acoustic emission has been experimentally investigated by the inventors of the present invention.

In the following section experimental data, obtained using various implementations of an acoustic monitoring system constructed and operative in accordance with preferred embodiments of the present invention, are presented. The experimental data relates to the monitoring of measurable acoustic emission generated by semiconductor devices on a PCB, the monitoring of measurable acoustic emission generated by a PCB hosting semiconductor devices and the monitoring of measurable acoustic emission generated by a bare IC chip, not mounted on a PCB. The experiments presented hereinbelow were repeated several times on different ones of identical devices.

It is appreciated that one or more of the semiconductor devices and ICs described hereinbelow are possible embodiments of semiconductor device <NUM> of <FIG>. Furthermore, it is understood that similar ones of semiconductor devices described hereinbelow may collectively form a population of semiconductor devices providing historical or present acoustic emission measurements for use by signal processing unit <NUM> in analyzing acoustic emission signals from a semiconductor device. Additionally, it is understood that the experimental set-ups described hereinbelow may be combined with signal processing unit <NUM>, real time alert unit <NUM> and feedback control unit <NUM> in order to allow identification and classification of operating states of the devices.

Reference is now made to <FIG>, which is a simplified block-diagram illustration showing an acoustic emission monitoring system, constructed and operative in accordance with a preferred embodiment of the present invention and used by the present inventors for measuring acoustic emission generated by one or more semiconductor components on a PCB.

As seen in <FIG>, there is provided an acoustic emission monitoring system <NUM> preferably including at least one acoustic emission sensor, here embodied, by way of example, as a first acoustic emission sensor <NUM> preferably in physical contact with a DUT <NUM> and a second acoustic emission sensor <NUM>, preferably not in physical contact with the DUT <NUM>. In the experiments reported hereinbelow, first acoustic emission sensor <NUM> was a <NUM> R15a ultrasonic sensor manufactured by MISTRAS of NJ, USA and second acoustic emission sensor <NUM> was an airborne ultrasonic microphone SPU410LR5H-QB manufactured by Knowles of IL, USA. First and second acoustic emission sensors <NUM> and <NUM> were each provided enclosed in a Faraday cage in order to shield them from EM radiation.

Here, DUT <NUM> is shown to be embodied as a PCB with at least one semiconductor component <NUM> mounted thereon, first acoustic emission sensor <NUM> preferably being in physical contact with semiconductor component <NUM>. It is appreciated that first acoustic emission sensor <NUM> thus preferably directly senses acoustic emission generated by semiconductor component <NUM>. Various examples of DUT <NUM> and/or semiconductor component <NUM> for which acoustic emission measurements were obtained by the present inventors using system <NUM> or variations thereof are described hereinbelow.

First acoustic emission sensor <NUM> is preferably connected to a first preamplifier <NUM>, which first preamplifier <NUM> is preferably connected to a first data acquisition unit <NUM> and a spectrum analyzer <NUM>. Second acoustic emission sensor <NUM> is preferably connected to a second preamplifier <NUM>, which second preamplifier <NUM> is connected to a second data acquisition unit <NUM> and an oscilloscope <NUM>. In the experiments reported hereinbelow, first and second data acquisition units <NUM> and <NUM> were NI-<NUM> data acquisition units, manufactured by National Instruments of Texas, USA. Spectrum analyzer <NUM> was an E4402B spectrum analyzer, manufactured by Keysight of California, USA. Oscilloscope <NUM> was an mso-x-2014a oscilloscope, manufactured by Agilent, of California, USA.

First and second preamplifiers <NUM> and <NUM> were each set to a gain of approximately <NUM> - <NUM> dB. The sampling frequency of first and second DAQs <NUM> and <NUM> was set to <NUM>. Low pass filters were connected upstream of first and second preamplifiers <NUM> and <NUM> for signal integrity testing.

Acoustic emission monitoring system <NUM> may optionally additionally include an infra-red sensor <NUM> for measuring the temperature of DUT <NUM> as well as for thermal mapping, and one or more antennas, here illustrated as a single antenna <NUM>, for measuring electromagnetic radiation generated by DUT <NUM> and/or electronic components thereon. One or more antennas <NUM> are preferably connected to a spectrum analyzer module <NUM>. Spectrum analyzers <NUM> and <NUM> had a <NUM> resolution bandwidth.

It is appreciated that the particular configuration of system <NUM> is illustrative only and may readily be modified by one skilled in the art to include a greater or fewer number of components, as exemplified hereinbelow. Furthermore, system <NUM> may include alternative components replacing the functionality of the illustrated components. For example, a single acoustic emission sensor rather than two acoustic emission sensors may be included in system <NUM>, first and second preamplifiers <NUM>, <NUM> may be obviated, the data acquisition units may be replaced by alternative sampling units, oscilloscope <NUM> and spectrum analyzer <NUM> may be obviated and so forth.

Additionally, although the acoustic emission sensors described as being employed in system <NUM> were ultrasound sensors, the monitored acoustic emission may additionally or alternatively be at frequencies lower than ultrasonic frequencies depending on the particular DUT, and appropriate acoustic emission sensors employed accordingly.

It is understood that an acoustic emission monitoring system such as system <NUM> may be incorporated within system <NUM>, system <NUM> or system <NUM> described hereinabove. Particularly, system <NUM> may form a part of a preferred embodiment of at least one acoustic sensor <NUM> and signal processing unit <NUM> in system <NUM> of <FIG> or of acoustic emission sensing modules <NUM> and <NUM> of <FIG> and <FIG> respectively.

An experimental set-up generally resembling that shown in <FIG> was used to monitor acoustic emission generated by a flash memory mounted on a PCB sampling circuit.

The PCB under test included thereon a 5V micro-CPU, consuming approximately <NUM> mA during operation, a 5V 16MB flash memory comprising floating gate MOSFET transistors, as well as various other electronic components including filters and converters. The PCB was a sampling circuit, designed for sampling external sensors at a <NUM> frequency, although no such sensors were connected to the PCB during the course of the experiment. First acoustic emission sensor <NUM> was directly mounted on the flash memory. Second microphone <NUM> was located at a distance of several cm and offset to the side of the flash memory.

Measured acoustic emission intensity as a function of time is displayed in a graph <NUM> in <FIG>. The data displayed in graph <NUM> corresponds to the raw data obtained after filtering with a high pass filter of <NUM> in order to reduce the environmental acoustic signal. It is noteworthy, however, that features of the signal identified hereinbelow were also visible after filtering of the raw data with only a <NUM> filter, albeit less clearly. The acoustic emission measured by first physically contacting emission sensor <NUM> is represented by a first trace <NUM> and the acoustic emission measured by second non-contacting microphone <NUM> is represented by a second trace <NUM>.

At the start of the experiment (t = <NUM>) the flash was in an idle state. The flash was put into operation at approximately t = <NUM> and returned to an idle state just prior to t = <NUM>, as indicated on the graph <NUM>. Enhanced acoustic emission is seen to be measured by both sensors <NUM>, <NUM> when the flash memory is in an active operating state in comparison to the acoustic emission when the flash memory is in an idle state. Additionally, a large burst in acoustic emission is seen in region <NUM>, corresponding to the wakeup state of the flash memory. This burst in acoustic emission is understood by the present inventors to be caused by bias settling. Furthermore, variations in acoustic emission in regions <NUM> and <NUM> are indicative of the CPU interrupt of the CPU to which the flash memory was connected.

As best seen at an enlargement <NUM> of region <NUM> showing the CPU interrupt, the variation in acoustic emission corresponding to the CPU interrupt spanned a time interval of approximately <NUM>, which time interval is consistent with the length of the CPU interrupt based on the specification of the CPU employed in this experiment. The CPU interrupt is exhibited in the acoustic emission spectra measured from the flash due to the connection of the CPU to the flash. As a result, the CPU influenced the flash acoustic emission.

The capability of detection of CPU interrupts by way of acoustic emission monitoring, in accordance with preferred embodiments of the present invention, is a highly advantageous feature of the present invention. Both the presence and duration of CPU interrupts are detectable based on the acoustic emission generated by the flash connected to the CPU. This allows classification and identification of CPU interrupts during flash memory operation and facilitates identification of developing faults leading to possible failure, as well as possible breaches in security of the flash memory, based on their influence on acoustic emission features associated with CPU interrupts.

As seen in an enlargement <NUM> of wake-up region <NUM> in <FIG>, the wake-up acoustic emission intensity as measured by first physically contacting sensor <NUM> is characterized by an initial exponential rise in intensity over a time period of approximately <NUM> followed by a gradual exponential decrease in intensity over a time period of approximately <NUM>. Peaks of decreasing intensity are mutually separated by a time gap of approximately <NUM>.

Features of the wakeup state acoustic emission spectra may be used for investigating the mechanism responsible for the generation of acoustic emission by the various components on the PCB. Particularly, the rise and fall times of the acoustic emission during wakeup are significantly lower than the time taken for the components on the PCB to heat, according to calculations by the present inventors based on the thermal diffusivity of the flash memory. This suggests that the acoustic emission arises directly from charge-lattice interactions, such as electron-phonon scattering, leading to atomic motion of the host lattice rather than being due to indirect heating effects. This understanding is supported by the fact that infra-red measurements of the flash memory housing during the course of the experiment show a generally constant housing temperature over the idle, wakeup and active states.

As seen in <FIG>, illustrating an enlargement of a region <NUM> of graph <NUM>, peaks in the wakeup region <NUM> are characterized by a dominant frequency of approximately <NUM>.

The frequency domain acoustic emission power spectra generated by the flash memory as measured by first physically contacting acoustic emission sensor <NUM> are displayed in <FIG>, <FIG> and <FIG>, respectively displaying the acoustic emission intensity spectra for the flash memory in an idle, actively operating and wakeup state. The actively operating state of the flash corresponds to the storing of data in the flash. The data displayed corresponds to the raw data in the frequency domain following application of a <NUM> high pass filter.

As clear from consideration of <FIG>, a significantly higher acoustic emission intensity was measured in the wakeup and operative states, in comparison to the idle state, with the wakeup and operative states having acoustic emission intensity of the order of <NUM>-<NUM> and <NUM>-<NUM> V<NUM> in contrast to the idle state having acoustic emission intensity of the order of <NUM>-<NUM> V<NUM>. Furthermore, the acoustic emission intensity spectra in the wakeup and operative states exhibit mutually difference acoustic emission features and signatures, allowing the acoustic emission spectra corresponding to the various operating states of the DUT to be readily identified and classified. Additionally, with respect to the wakeup state intensity spectrum displayed in <FIG>, an intensity peak is seen at approximately <NUM>, corresponding to the dominant frequency of <NUM> visible in <FIG>. The dominant frequency associated with the wakeup state of the flash memory is an additional feature of the acoustic emission that may be used in order to identify the flash memory operating state and detect anomalies thereof.

The frequency domain acoustic emission intensity spectra generated by the flash memory as measured by second non-physically contacting microphone <NUM> are displayed in <FIG>, <FIG> and <FIG>, respectively displaying the acoustic emission intensity spectra for the flash memory in an idle, actively operating and wakeup state. The data displayed corresponds to the raw data in the frequency domain following application of a <NUM> high pass filter.

As clear from consideration of <FIG>, a significantly higher acoustic emission intensity was measured by microphone <NUM> in the wakeup and operative states, in comparison to the idle state. Furthermore, the acoustic emission intensity spectra in the wakeup and operative states exhibit mutually difference acoustic emission features and signatures, allowing the acoustic emission spectra corresponding to the various operating states of the DUT to be readily identified and classified. Additionally, with respect to the wakeup state spectrum displayed in <FIG>, an intensity peak is seen at approximately <NUM>, corresponding to the dominant frequency of <NUM> visible in <FIG>. The dominant frequency associated with the wakeup state of the flash memory is an additional feature of the acoustic emission that may be used in order to identify the flash memory operating state and detect anomalies thereof.

As appreciated from a comparison of <FIG> to <FIG>, the acoustic emission intensity as measured by physically contacting sensor <NUM> is greater than and different to that measured by non-physically contacting microphone <NUM>. This is attributable to attenuation of the signal over the distance between the microphone <NUM> and the flash memory, the acoustic transfer function that is orientation dependent with respect to the microphone and possible blocking of the acoustic emission by the sensor <NUM> due to sensor <NUM> resting on the surface of the flash memory.

Statistical features of the acoustic emission generated by the flash memory in various operating states thereof, as measured by the acoustic emission sensor <NUM> and non-contacting microphone <NUM>, are tabulated in Tables <NUM> and <NUM> respectively.

As appreciated from consideration of the values presented in Tables <NUM> and <NUM>, large variations are seen in statistical features of acoustic emission generated by the flash memory in various operating states thereof, as measured by both physically and non-physically contacting acoustic emission sensors. These large variations in statistical features associated with acoustic emission in the various operating states of the device, allow the use of statistical analysis and machine-learning algorithms for automatically classifying an operating state of the device as well as identifying possible anomalies in the acoustic emission features arising from faulty or malicious device operation.

As seen in Tables <NUM> and <NUM>, the rms and standard deviation values in each case are generally equal, since the mean value of the measured acoustic emission is approximately zero.

Probability distribution functions reflecting the statistical features of the various operating states of the flash memory, as measured by the sensor <NUM> and microphone <NUM>, are displayed in <FIG> respectively. The probability distribution functions illustrate variations in statistical features of the spectra associated with the various operating states, allowing the use of higher order statistical moments as well as cumulants and other statistical measures for differentiating between and identifying various operating states of the device under test.

The electromagnetic power generated during various operating states of the circuit on the PCB, as measured by passive antenna <NUM>, is displayed in <FIG> and <FIG>, showing power in the frequency and time domain respectively. As seen in <FIG>, the electromagnetic power generated by the flash memory in an active operating state, as represented by a first trace <NUM>, is significantly higher than the electromagnetic power generated by the flash memory in an idle state, as represented by a second trace <NUM>. An increase in electromagnetic power of approximately 3dBm is seen at a frequency range of about <NUM>. This frequency range is consistent with the dominant frequency seen in the acoustic emission signature displayed in <FIG>, indicating a correlation between features of the electromagnetic emission and the acoustic emission. Furthermore, as seen in a region <NUM> of <FIG>, which region <NUM> corresponds to the active operating state of the flash memory, the electromagnetic power in the time domain is seen to be significantly enhanced during active operation in comparison to the idle state.

The enhancement in electromagnetic radiation in the active state of the flash memory is believed to be due to electric currents and acceleration of charges within the device, which electric currents and accelerating charges interact with the host lattice to produce both electromagnetic and acoustic emission. The correlation between acoustic emission and electromagnetic radiation generated by the circuit during operation thereof may be used to identify operating states of the flash memory as well as to detect developing faults or failure of the flash memory based on features of both the acoustic and electromagnetic emission.

An experimental set-up generally resembling that shown in <FIG> was used to monitor acoustic emission generated by a CPU mounted on a PCB sampling circuit.

The PCB under test included thereon a 5V micro-CPU, consuming approximately <NUM> mA during operation, a 5V 16MB flash memory comprising floating gate MOSFET transistors, as well as various other electronic components including filters and converters. The PCB was a sampling circuit, designed for sampling external sensors at a <NUM> frequency, although no such sensors were connected to the PCB during the course of the experiment. First acoustic emission sensor <NUM> was directly mounted on the CPU. It is appreciated that first acoustic emission sensor <NUM> thus senses acoustic emission directly from the CPU, which acoustic emission is generated by the CPU itself. Second microphone <NUM> was located at a distance of several cm and offset to the side with respect to the CPU.

At the start of the experiment (t = <NUM>) the CPU was in an idle state. The CPU was put into operation shortly following t = <NUM> and returned to an idle state shortly after t = <NUM>, as indicated on the graph <NUM>. Enhanced acoustic emission is seen to be measured by both sensors <NUM>, <NUM> when the CPU is in an active operating state in comparison to the acoustic emission when the CPU is in an idle state. Additionally, a large burst in acoustic emission is seen in region <NUM>, corresponding to the wakeup state of the CPU. This burst in acoustic emission is understood by the present inventors to be caused by bias settling. Furthermore, variations in acoustic emission in regions <NUM> are indicative of the CPU interrupt. As best seen at an enlargement <NUM> of region <NUM> showing the CPU interrupt, the variation in acoustic emission corresponding to the CPU interrupt spanned a time interval of approximately <NUM>, which time interval is consistent with the length of the CPU interrupt based on the specification of the CPU employed in this experiment.

The capability of detection of CPU interrupts by way of acoustic emission monitoring, in accordance with preferred embodiments of the present invention, is a highly advantageous feature of the present invention. Both the presence and duration of CPU interrupts are detectable based on the acoustic emission generated thereby. This allows classification and identification of CPU interrupts during operation and facilitates identification of developing faults leading to possible failure, as well as possible breaches in security of the CPU, based on their influence on acoustic emission features associated with CPU interrupts.

As seen in an enlargement <NUM> in <FIG> of wake-up region <NUM>, the wake-up acoustic emission as measured by first physically contacting sensor <NUM> is characterized by an initial exponential rise in intensity over a time period of approximately <NUM> followed by a gradual exponential decrease in intensity over a time period of approximately <NUM>. Peaks of decreasing intensity are mutually separated by a time gap of approximately <NUM>.

Features of the wakeup state acoustic emission spectra may be used for investigating the mechanism responsible for the generation of acoustic emission by the CPU. Particularly, the rise and fall times of the acoustic emission during wakeup are significantly lower than the time taken for the PCB hosting the CPU to heat, according to calculations by the present inventors based on the thermal diffusivity of the CPU. This suggests that the acoustic emission arises directly from charge-lattice interactions, such as electron-phonon scattering, leading to atomic motion of the host lattice rather than being due to indirect heating effects.

The frequency domain acoustic emission intensity spectra generated by the CPU as measured by first physically contacting acoustic emission sensor <NUM> are displayed in <FIG>, <FIG> and <FIG>, respectively displaying the power spectra for the flash memory in an idle, actively operating and wakeup state. The data displayed corresponds to the raw data in the frequency domain following application of a <NUM> high pass filter.

As clear from consideration of <FIG>, a significantly higher acoustic emission power was measured in the wakeup and operative states, in comparison to the idle state, with the wakeup and operative states having acoustic emission intensity of the order of <NUM>-<NUM> and <NUM>-<NUM> V<NUM> in contrast to the idle state having acoustic emission intensity of the order of <NUM>-<NUM> V<NUM>. Furthermore, the acoustic emission intensity spectra in the wakeup and operative states exhibit mutually difference acoustic emission features and signatures, allowing the acoustic emission spectra corresponding to the various operating states of the DUT to be readily identified and classified. Additionally, with respect to the wakeup state intensity spectrum displayed in <FIG>, a power peak is seen at approximately <NUM>, corresponding to the dominant frequency of <NUM> visible in <FIG>. The dominant frequency associated with the wakeup state of the CPU is an additional feature of the acoustic emission that may be used in order to identify the CPU operating state and detect anomalies thereof.

The frequency domain acoustic emission power spectra generated by the CPU as measured by second non-physically contacting microphone <NUM> are displayed in <FIG>, respectively displaying the power spectra for the CPU in a wakeup, actively operating and idle state. The data displayed corresponds to the raw data in the frequency domain following application of a <NUM> high pass filter.

As clear from consideration of <FIG>, a significantly higher acoustic emission intensity was measured by microphone <NUM> in the wakeup and operative states, in comparison to the idle state. Furthermore, the acoustic emission intensity spectra in the wakeup and operative states exhibit mutually difference acoustic emission features and signatures, allowing the acoustic emission spectra corresponding to the various operating states of the DUT to be readily identified and classified. Additionally, with respect to the wakeup state spectrum displayed in <FIG>, a power peak is seen at approximately <NUM>, corresponding to the dominant frequency of <NUM> visible in <FIG>. The dominant frequency associated with the wakeup state of the CPU is an additional feature of the acoustic emission that may be used in order to identify the CPU operating state and detect anomalies thereof.

As appreciated from a comparison of <FIG> to <FIG>, the acoustic emission intensity as measured by physically contacting sensor <NUM> is greater than and different to that measured by non-physically contacting microphone <NUM>. This is attributable to attenuation of the signal over the distance between the microphone <NUM> and the CPU, the acoustic transfer function that is orientation dependent with respect to the microphone and possible blocking of the acoustic emission by the sensor <NUM> due to sensor <NUM> resting on the surface of the CPU.

Statistical features of the acoustic emission generated by the CPU in various operating states thereof, as measured by the acoustic emission sensor <NUM> and non-contacting microphone <NUM>, are tabulated in Tables <NUM> and <NUM> respectively.

As appreciated from consideration of the values presented in Tables <NUM> and <NUM>, large variations are seen in some statistical features of acoustic emission generated by the CPU in various operating states thereof, as measured by both physically and non-physically contacting acoustic emission sensors. These large variations in statistical features associated with acoustic emission in the various operating states of the device, allow the use of statistical analysis and machine-learning algorithms for automatically classifying an operating state of the device as well as identifying possible anomalies in the acoustic emission features arising from faulty or malicious device operation.

Probability distribution functions reflecting the statistical features of the various operating states of the CPU, as measured by the sensor <NUM> and microphone <NUM>, are displayed in <FIG> respectively. The probability distribution functions illustrate variations in statistical features of the spectra associated with the various operating states, allowing the use of higher order statistical moments as well as cumulants and other statistical measures for differentiating between and identifying various operating states of the device under test and detecting faulty operation and incipient failure thereof.

An experimental set-up generally resembling that shown in <FIG>, but including only physically contacting sensor <NUM> connected to a preamplifier and spectrum analyzer, was used to monitor acoustic emission generated by a CPU mounted on a PCB Odroid board.

Acoustic emission energy as a function of frequency as measured by sensor <NUM> when the CPU was in an on state and an off state is presented in <FIG>. As seen in <FIG>, the acoustic emission energy during the off state of the CPU is represented by a first trace <NUM> and the acoustic emission energy during the on state of the CPU is represented by a second trace <NUM>. As appreciated from a comparison of traces <NUM> and <NUM>, the acoustic emission energy is seen to increase significantly during operation of the CPU in comparison to when the CPU is off. This indicates that acoustic emission monitoring of low power devices such as a CPU on a commercial PCB may be useful for identifying operating states of the device and detecting development or presence of faults in the device based on deviations from predetermined or machine-learned features associated with the acoustic emission generated during various states of the device operation.

Reference is now made to <FIG>, which is a simplified block-diagram illustration showing an acoustic emission monitoring system, constructed and operative in accordance with another preferred embodiment of the present invention and used by the present inventors for measuring acoustic emission generated by one or more electronic components on a PCB, based on measurements made from the PCB itself.

As seen in <FIG>, there is provided an acoustic emission monitoring system <NUM> preferably including at least one acoustic emission sensor, here embodied, by way of example, as a first acoustic emission sensor <NUM> preferably in physical contact with a DUT <NUM> and a second acoustic emission sensor <NUM>, preferably not in physical contact with the DUT <NUM>. In the experiments reported hereinbelow, first acoustic emission sensor <NUM> was a <NUM> R15a ultrasonic sensor manufactured by MISTRAS of NJ, USA and second acoustic emission sensor <NUM> was an airborne ultrasonic microphone SPU410LR5H-QB manufactured by Knowles of IL, USA. First and second acoustic emission sensors <NUM> and <NUM> were each enclosed in a Faraday cage in order to shield them from EM radiation.

Here, DUT <NUM> is shown to be embodied as a PCB with at least one electronic component <NUM> mounted thereon, first acoustic emission sensor <NUM> preferably being in physical contact with surface of the PCB itself , rather than directly with the electronic component <NUM>. It is appreciated, however, that sensor <NUM> may additionally or alternatively be in direct physical contact with at least one of electronic components <NUM>, in addition to with the PCB board <NUM> itself.

First acoustic emission sensor <NUM> is preferably connected to a data acquisition unit <NUM>. Second acoustic emission sensor <NUM> is preferably connected to a preamplifier <NUM>, which preamplifier <NUM> is preferably connected to a spectrum analyzer <NUM>. In the experiments reported hereinbelow, data acquisition unit <NUM> was an NI-<NUM> data acquisition unit, manufactured by National Instruments of Texas, USA. Spectrum analyzer <NUM> was an E4402B spectrum analyzer, manufactured by Keysight of California, USA having a <NUM> resolution bandwidth.

Preamplifier <NUM> was set to a gain of approximately <NUM> - <NUM> dB. The sampling frequency of DAQ <NUM> was set to <NUM>. A low pass filter was connected upstream of preamplifier <NUM> for signal integrity testing.

An experimental set-up generally resembling that shown in <FIG> was used to monitor acoustic emission generated by a PCB-mounted microcontroller with an operating voltage of <NUM> V. Acoustic emission sensor <NUM> was located directly on top of the microcontroller, so as to be in direct physical contact therewith.

Acoustic emission generated by the microcontroller was monitored during two operational states: a powered 'on' state and a non-powered 'off' state. Acoustic emission intensity as a function of time and frequency is displayed in <FIG> and <FIG> respectively, as measured by physically contacting sensor <NUM>. As seen both in the time domain (<FIG>) and frequency domain (<FIG>), measured acoustic emission in an 'on' state, as represented by reference numeral <NUM> is significantly enhanced in comparison to measured acoustic emission in an 'off' state, as represented by reference numeral <NUM>.

Acoustic emission as a function of frequency is displayed in <FIG>, as measured by microphone <NUM>. The acoustic emission sensor within the microphone was approximately <NUM> distant from the microcontroller. Acoustic emission generated by the microcontroller was monitored during four operational states of the microcontroller: a non-powered 'off' state, a 'sleep' mode, an 'idle' mode and an active 'sampling' mode.

As seen in <FIG>, acoustic emission when the device is in an off mode is represented by a first trace <NUM>, acoustic emission when the device is in a sleep mode is represented by a second trace <NUM>, acoustic emission when the device is in an idle mode is represented by a third trace <NUM> and acoustic emission when the device is in an active sampling mode is represented by a forth trace <NUM>.

As appreciated from a comparison of traces <NUM> - <NUM>, acoustic emission when the microcontroller is in a powered state is significantly enhanced in comparison to acoustic emission when the device is in an off or sleep mode. Furthermore, acoustic emission energies are seen to differ between the idle and sleep modes, with the microcontroller generating significantly less acoustic emission in the sleep mode in comparison to the idle mode. These results indicate that measurable differences in acoustic emission may be detected and thus used to distinguish between different operational states of a microcontroller mounted on a PCB in the case of the acoustic emission being measured by a non-physically contacting acoustic emission sensor.

These results indicate that measurable differences in acoustic emission may be detected and thus used to distinguish between on and off operational states of a microcontroller device mounted on a PCB. It is understood that the measured acoustic emission originates with the microcontroller device in both the on and off states thereof. The microcontroller is thus itself the source of the acoustic emission and is not simply a reflecting a portion of an acoustic signal received from an external source.

An experimental set-up generally resembling that shown in <FIG> was used to monitor a PCB hosting a <NUM>. 3V micro-controller in addition to a variety of low-power electrical components including lumped components, an amplifier and analogue-to-digital converter. Sensor <NUM> was located atop of the underside of the PCB, so as to be in direct physical contact therewith. The electrical circuit included in the PCB was designed to transfer acquired signal from the ultrasound sensor to a mobile phone. The sampling frequency of the data acquisition unit was set to <NUM>.

Acoustic emission by the PCB was monitored during a powered operating state and a non-powered 'off state', as well as during intermediate 'wakeup', 'sleep' and 'shutdown' states.

Measured acoustic emission intensity as a function of frequency and time is displayed in <FIG> and <FIG> respectively. As seen both in the frequency domain (<FIG>) and time domain (<FIG>), measured acoustic emission during operation <NUM> of the PCB is significantly enhanced in comparison to measured acoustic emission when the PCB is off <NUM>. An increase of <NUM>% was found between the rms of the PCB 'on' state in comparison to that of the PCB 'off' state. Furthermore, an increase of <NUM>% was found between the rms of the PCB 'wakeup', 'sleep' and 'shutdown' states in comparison to that of the PCB 'off' state. The statistical moments of the signal were also found to differ between the various states.

The significant increase in AE emission during wakeup indicates that changes in AE emission may reflect electrical instability in a monitored device or PCB. It is particularly noteworthy that measurable differences between AE emission in the various operational states were found, despite ultrasonic damping and smearing effects expected to be caused by the PCB itself. These results indicate that measurable differences in acoustic emission may be detected and thus used to distinguish between different operational states of semiconductor components on a PCB.

An experimental set-up generally resembling that shown in <FIG> was used to monitor a PCB hosting a <NUM>. 3V micro-controller in addition to a variety of low-power electrical components including lumped components, an amplifier and analogue-to-digital converter. Sensor <NUM> was located atop of the underside of the PCB, so as to be in direct physical contact therewith. The electrical circuit included in the PCB was designed to transfer acquired signal from the ultrasound sensor to a mobile phone. The sampling frequency of the data acquisition unit was set to <NUM>. A <NUM> Ohm resistor was connected to a power source and to the PCB ground.

Acoustic emission by the PCB was monitored during two operational states: a first operating state in which the resistor was non-powered and a second operating state in which the resistor was powered and thereby heated, thus exerting thermal stress on the PCB.

Measured acoustic emission intensity as a function of time is displayed in <FIG>. Spikes in measured acoustic emission are seen in the time domain as well as changes in the statistical moments of the measured signal. These spikes may be attributable to temperature fluctuations or to voltage fluctuations caused by instability in the PCB grounding. The presence of these spikes support the hypothesis that electrical instability influences AE spectral energy, and thus changes in AE spectral energy and statistical features thereof may be used to detect or predict electrical failure.

Acoustic emission by the PCB was monitored during two operational states: a first regular operating state and a second operating state in which mechanical stress was applied to the PCB by way of clamping. In the second operating state, the lateral surface of the PCB was embedded between clamps and a mechanical stress in the range of several Newtons applied to the PCB, resulting in the bending of the PCB. Acoustic emission measurements were taken at a steady state for which the applied force was constant.

Measured acoustic emission intensity as a function of time is displayed in <FIG>. As seen most clearly in the time domain in <FIG>, measured acoustic emission during operation of the PCB under mechanical stress is enhanced in comparison to measured acoustic emission when mechanical stress is not applied. An increase of <NUM>% was found between the rims of the PCB mechanically-stressed state in comparison to that of the PCB regular, non-stressed state. Furthermore, significant statistical differences were found between the signal distributions in the two states, including a difference in skewness of <NUM>%.

These results indicate that the exertion of mechanical stress on a PCB hosting low-power semiconductor devices leads to measurable changes in acoustic emission generated thereby. Changes in acoustic emission, including changes in the statistical distribution of acoustic emission, may therefore be used to detect mechanical stress and predict possible consequent electrical failure.

Reference is now made to <FIG>, which is a simplified block-diagram illustration showing an acoustic emission monitoring system, constructed and operative in accordance with a preferred embodiment of the present invention and used by the present inventors for measuring acoustic emission generated by one or more bare electronic components, not mounted on a PCB.

As seen in <FIG>, there is provided an acoustic emission monitoring system <NUM> preferably including at least one acoustic emission sensor, here embodied, by way of example, as a first acoustic emission sensor <NUM> preferably in physical contact with a DUT <NUM> and a second acoustic emission sensor <NUM>, preferably not in physical contact with the DUT <NUM>. In the experiments reported hereinbelow, first acoustic emission sensor <NUM> was a R15a ultrasonic sensor manufactured by MISTRAS of NJ, USA and second acoustic emission sensor <NUM> was an airborne ultrasonic microphone manufactured by Knowles of IL, USA. First and second acoustic emission sensors <NUM> and <NUM> were each provided enclosed in a Faraday cage in order to shield them from EM radiation.

Here, DUT <NUM> is shown to be embodied as a semiconductor device such as a bare flash memory. First acoustic emission sensor <NUM> is preferably connected to a first preamplifier <NUM>, which first preamplifier <NUM> is preferably connected to a first data acquisition unit <NUM> and a spectrum analyzer <NUM>. Second acoustic emission sensor <NUM> is preferably connected to a second preamplifier <NUM>, which second preamplifier <NUM> is preferably connected to a second data acquisition unit <NUM> and an oscilloscope <NUM>. In the experiments reported hereinbelow, first and second data acquisition units <NUM> and <NUM> were NI-<NUM> data acquisition units, manufactured by National Instruments of Texas, USA. Spectrum analyzer <NUM> was an E4402B spectrum analyzer, manufactured by Keysight of California, USA. Oscilloscope <NUM> was a mso-x-2014a oscilloscope, manufactured by Agilent, of California, USA.

Acoustic emission monitoring system <NUM> may optionally additionally include an infra-red sensor <NUM> for measuring the temperature of DUT <NUM> and one or more antennas, here illustrated as a single antenna <NUM>, for measuring electromagnetic radiation generated by DUT <NUM> and/or electronic components thereon. One or more antennas <NUM> are preferably connected to a spectrum analyzer module <NUM>.

It is appreciated that the particular configuration of system <NUM> is illustrative only and may readily be modified by one skilled in the art to include a greater or fewer number of components, as exemplified hereinbelow. Furthermore, system <NUM> may include alternative components replacing the functionality of the illustrated components. For example, a single acoustic emission sensor rather than two acoustic emission sensors may be included in system <NUM>, first and second preamplifiers <NUM>, <NUM> may be obviated, the data acquisition units may be replaced by alternative sampling units, oscilloscope <NUM> may be replaced by a spectrum analyzer, spectrum analyzer <NUM> may be replaced by an oscilloscope having a fast fourier transform function, additional or alternative frequency filters may be included and so forth.

An experimental set-up generally resembling that shown in <FIG> was used to monitor acoustic emission generated by a bare flash memory. The flash memory was connected to a microprocessor mounted on an external PCB. The flash memory under test comprised floating gate MOSFET transistors. It is appreciated that acoustic emission sensor <NUM> thus directly measured acoustic emission from the flash memory, which acoustic emission is generated by the flash memory itself.

Acoustic emission generated by the flash memory during an idle state and an erase state is presented in <FIG>, for acoustic emission measurements by both physically contacting sensor <NUM> and microphone <NUM>. At the start of the experiment (t = <NUM>) the flash memory was in an idle state. The flash memory was put into an erase state at approximately t = <NUM> and returned to an idle state at approximately t = <NUM>, as indicated in <FIG>.

In the case of acoustic emission measurements by both of sensors <NUM> and <NUM>, an increase in acoustic emission is seen in the erase state in comparison to the idle state. The increase in acoustic emission during the erase state is believed by the present inventors to be associated with the field tunneling process, involving charge-lattice interactions responsible for the generation of acoustic emission. Furthermore, the duration of the increased emission corresponding to the flash erase state is in accordance with the erase duration provided in the flash specification. Additionally, high acoustic emission variation is seen at regions <NUM> and <NUM>, at times of switching between states, which high variation is believed to be due to voltage settling at these times.

The signal from the spectrum analyzer corresponding to the acoustic emission measured by acoustic emission sensor <NUM> is displayed in <FIG>. The resolution bandwidth of the spectrum analyzer was <NUM>. As seen in <FIG>, enhance acoustic emission is visible for the active erase state, indicated by a first trace <NUM>, in comparison the acoustic emission in the idle state, indicated by a second trace <NUM>.

A probability distribution function reflecting the statistical features of the various operating states of the flash, as measured by the sensor is displayed in <FIG>. The probability distribution function illustrates variations in statistical features of the spectra associated with the various operating states, allowing the use of higher order statistical moments as well as cumulants and other statistical measures for differentiating between and identifying various operating states of the device under test and detecting faulty operation and incipient failure thereof.

The electromagnetic power generating during various operating states of the flash memory, as measured by passive antenna <NUM>, is displayed in <FIG> and <FIG>, showing power in the time and frequency domain respectively. As seen in <FIG>, the electromagnetic power generated by the flash memory in an active erase state, as represented by a first trace <NUM>, is significantly higher than the electromagnetic power generated by the flash memory in an idle state, as represented by a second trace <NUM>. An increase in electromagnetic power of approximately 8dBm is seen at a frequency range of about <NUM>. This frequency range is consistent with the dominant frequency seen in the acoustic emission signature displayed in <FIG>, indicating a correlation between features of the electromagnetic emission and the acoustic emission. Furthermore, as seen in a region <NUM> of <FIG>, which region <NUM> corresponds to the active erase state of the flash memory, the electromagnetic power in the time domain is seen to be significantly enhanced during active operation in comparison to the idle state.

The enhancement in electromagnetic radiation in the erase state of the flash memory is believed to be due to electron tunneling within the device, which tunneling generates electromagnetic radiation, in addition to the interaction of electrons with the host lattice producing both electromagnetic and acoustic emission. The correlation between acoustic emission and electromagnetic radiation generated by the flash memory during operation thereof may be used to identify operating states of the flash memory as well as to detect developing faults or failure of the flash memory based on features of both the acoustic and electromagnetic emission.

Acoustic emission measured by sensor <NUM> during write operation by the flash is displayed in <FIG> and acoustic emission measured by sensor <NUM> during read operation by the flash is displayed in <FIG>. As seen in <FIG> at regions <NUM> therein, the read and write state switching is accompanied by large fluctuations in acoustic emission. However, the two states may be differentiated by the longer duration of the write state as well as by the higher energy switching of the write state in comparison to the read state.

The unstable signal seen during the read and write states may be attributable to the large drain-source current in these states. Since this current is oriented perpendicular to the direction of measurement, instabilities may be seen in the acoustic emission arising from the current.

The frequency domain acoustic emission intensity spectra generated by the flash as measured by physically contacting sensor <NUM> are displayed in <FIG>, respectively displaying the intensity spectra for the flash memory in wakeup, write and idle states. The frequency domain acoustic emission intensity spectra generated by the flash as measured by physically contacting sensor <NUM> are displayed in <FIG>, respectively displaying the intensity spectra for the flash memory in wakeup, read and idle states. As clear from consideration of <FIG>, the various operating states are distinguished by different acoustic emission signatures having different statistical moments. In particular, the acoustic emission in the write mode is of higher amplitude than the acoustic emission in the read mode. This supports the hypothesis that the acoustic emission arises from the drain-source current, since the drain source current is lower in the read mode than in the write mode.

<FIG>, <FIG> and <FIG> illustrate acoustic emission intensity in the frequency domain, generated by the flash memory and measured by microphone <NUM> for the idle, operative write and wakeup states. As clearly seen in <FIG>, the acoustic emission intensity spectra in the wakeup and operative write states exhibit mutually difference acoustic emission features and signatures, allowing the acoustic emission spectra corresponding to the various operating states of the flash to be readily identified and classified. The high acoustic energy at approximately <NUM> and <NUM> seen in <FIG> is associated with a line-bit write of approximately <NUM> microseconds, in accordance with values provided in the flash specification. The <NUM> and <NUM> peaks thus may be understood as attributable to drain source current in the flash during the write operation. Side bands at approximately <NUM> are seen on either side of the <NUM> and <NUM> peaks, indicative of page write periods and associated with a page write time of approximately <NUM> microseconds, which duration is in keeping with values provided in the flash specification. It is noteworthy that a corresponding <NUM> intensity peak was also found to be present in the acoustic emission sensor <NUM> signal for the write operative state, although this is not seen in <FIG> due to the limited bandwidth displayed therein.

<FIG> illustrates acoustic emission arising from the flash memory in the case that the flash memory was programmed to work over an infinite loop of write/erase for a specific sector. In this case, measured acoustic emission was passed to a spectrum analyzer, where the spectrum analyzer was configured to measure at a central frequency of <NUM>, with <NUM> span and resolution bandwidth of <NUM>. The measured signal was averaged over the span. It would be expected that the repetitive erase process would create oxide breakdown zones.

Claim 1:
A system for monitoring and identifying states of a semiconductor device (<NUM>, <NUM>, <NUM>), the system comprising:
at least one semiconductor device operating at a voltage of less than or equal to 220V, the semiconductor device being one of a microcontroller device (<NUM>) or a flash memory device (<NUM>) comprising floating gate MOSFET transistors;
at least one acoustic sensor (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) for sensing acoustic emission (<NUM>) emitted by the at least one semiconductor device, said at least one acoustic sensor outputting at least one signal representative of said acoustic emission; and
a signal processing unit (<NUM>) for receiving said at least one signal representative of said acoustic emission from said at least one acoustic sensor and for analyzing said at least signal representative of acoustic emission,
said signal processing unit providing an output (<NUM>) based on said analyzing, said output being indicative at least of whether said at least one semiconductor device is in an abnormal operating state with respect to a normal operating state of said semiconductor device,
wherein:
said output is also indicative of whether said semiconductor device is in an active operating state or an idle operating state.