LH
L. Hamburger
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
BTI in SRAM
Mitigation for BTI ageing in SRAM memories
The aggressive downscaling of the transistor has led to gigantic improvements in the performance and func- tionality of electronics. As a result, electronics have become a significant part in our daily lives whose absence would be difficult to imagine. Our cars, for example, now consist of many sensors and small computers each controlling certain parts of the car. A downside of the aggressive downscaling of transistor sizes is that it nega- tively impacts the reliability and accelerated ageing, and thus a reduced lifetime, of electronics. Nevertheless, to ensure the reliable operation of electronics, it has therefore become essential to assess the reliability of any of its embedded components accurately. Conventionally, to combat ageing, designers use guardbanded design; adding design margins. These margins, however, lead to a penalty in area, power, and speed. Al- ternatively, one may investigate mitigation schemes that aim at reducing the impact of ageing to extend the reliability and lifetime. These mitigation schemes may lead to a higher performance compared with the con- ventional guardbanded design. This work focuses on an ageing mitigation scheme for SRAMs. SRAMs typi- cally have the highest contribution to the total area of integrated circuits. Therefore, they are highly optimised (i.e. their integration density is the lowest). This also makes them one of the most susceptible components to ageing. Hence, providing appropriate ageing mitigation schemes for SRAMs is essential for the overall reliability of ICs. Whereas prior work has mainly investigated hardware-based ageing mitigation schemes for SRAMs, this thesis investigates the possibility of mitigating the ageing through software. The advantages of this approach include that it does not require circuit changes (and, thus applicable to existing circuits) and it comes at zero area overhead. This study’s proposed software-based scheme is based on periodically running a mitigation routine. This mitigation scheme flips the contents of the memory cells to put the transistors into relaxation from BTI stress, the most crucial ageing mechanism in deeply scaled CMOS process. The results show that the software-based scheme can significantly reduce the ageing of the memory at a low overhead. For example, the degradation of the hold SNM metric of the memory cell is reduced with up to 40% at a runtime overhead of only 1.4%. Moreover, the scheme also mitigates the ageing of other components of the memory. For example, the degradation of the offset voltage of the sense amplifier is reduced by nearly 50%. This thesis shows that it is possible to use software to mitigate the ageing effects in the memory components and it is worthwhile to consider implementing it.
...
The aggressive downscaling of the transistor has led to gigantic improvements in the performance and func- tionality of electronics. As a result, electronics have become a significant part in our daily lives whose absence would be difficult to imagine. Our cars, for example, now consist of many sensors and small computers each controlling certain parts of the car. A downside of the aggressive downscaling of transistor sizes is that it nega- tively impacts the reliability and accelerated ageing, and thus a reduced lifetime, of electronics. Nevertheless, to ensure the reliable operation of electronics, it has therefore become essential to assess the reliability of any of its embedded components accurately. Conventionally, to combat ageing, designers use guardbanded design; adding design margins. These margins, however, lead to a penalty in area, power, and speed. Al- ternatively, one may investigate mitigation schemes that aim at reducing the impact of ageing to extend the reliability and lifetime. These mitigation schemes may lead to a higher performance compared with the con- ventional guardbanded design. This work focuses on an ageing mitigation scheme for SRAMs. SRAMs typi- cally have the highest contribution to the total area of integrated circuits. Therefore, they are highly optimised (i.e. their integration density is the lowest). This also makes them one of the most susceptible components to ageing. Hence, providing appropriate ageing mitigation schemes for SRAMs is essential for the overall reliability of ICs. Whereas prior work has mainly investigated hardware-based ageing mitigation schemes for SRAMs, this thesis investigates the possibility of mitigating the ageing through software. The advantages of this approach include that it does not require circuit changes (and, thus applicable to existing circuits) and it comes at zero area overhead. This study’s proposed software-based scheme is based on periodically running a mitigation routine. This mitigation scheme flips the contents of the memory cells to put the transistors into relaxation from BTI stress, the most crucial ageing mechanism in deeply scaled CMOS process. The results show that the software-based scheme can significantly reduce the ageing of the memory at a low overhead. For example, the degradation of the hold SNM metric of the memory cell is reduced with up to 40% at a runtime overhead of only 1.4%. Moreover, the scheme also mitigates the ageing of other components of the memory. For example, the degradation of the offset voltage of the sense amplifier is reduced by nearly 50%. This thesis shows that it is possible to use software to mitigate the ageing effects in the memory components and it is worthwhile to consider implementing it.
Target localisation and tracking in a UWB radar network
UWB Indoor Person Tracking
For both security and analytics, much research has gone into person tracking already. As a result, many
different state of the art technologies exist. However, in darkness or without a direct line of sight, much
less technologies are capable of this. The choices become especially limited when the setup needs to
be portable.
A method for person localisation and tracking is implemented. This method consists of a localisation
part, which works with any range-based detection method. Least square estimation is used to determine
the location from the radar detections. With two or more people, it is mathematically impossible to
distinguish which locations are correct, if only the current measurement is taken into account.
Thus, the first problem to be solved is connecting ranges to targets. This is done using target association.
After this is done, one-dimensional tracking can track people at lower computational cost.
The tracking is both in one dimension (per-radar) and in two dimensions. The Hungarian algorithm
is used for keeping track of people using a Kalman filter. The Kalman filter considers the predicted
next location and the measured next location, and makes a best guess. A neural network was used for
the optimisation of location-specific noise parameters, something that has not been done before in this
context. Single person tracking and two person tracking works as expected. The tracking is relatively
cheap in terms of computational complexity. While the tracking has no limits on the maximum number
of people present, the localisation gets increasingly difficult with a complexity of O (n^n). Detecting
the correct peaks is a non-trivial problem because of multi-path reflections. In combination with UWB
radar detections, single and dual person tracking in a room is achieved. More people can be handled by
the tracking algorithm, which is detection-method-agnostic, but not by the localisation. There is some
room for improvement in the dual and triple-person case. However, going further than this is currently
unfeasible, because of the many reflections that occur. Furthermore, the large amount of possible person
locations also has an effect. This is a problem that scales with O (n^n) where n is the amount of
targets. ...
different state of the art technologies exist. However, in darkness or without a direct line of sight, much
less technologies are capable of this. The choices become especially limited when the setup needs to
be portable.
A method for person localisation and tracking is implemented. This method consists of a localisation
part, which works with any range-based detection method. Least square estimation is used to determine
the location from the radar detections. With two or more people, it is mathematically impossible to
distinguish which locations are correct, if only the current measurement is taken into account.
Thus, the first problem to be solved is connecting ranges to targets. This is done using target association.
After this is done, one-dimensional tracking can track people at lower computational cost.
The tracking is both in one dimension (per-radar) and in two dimensions. The Hungarian algorithm
is used for keeping track of people using a Kalman filter. The Kalman filter considers the predicted
next location and the measured next location, and makes a best guess. A neural network was used for
the optimisation of location-specific noise parameters, something that has not been done before in this
context. Single person tracking and two person tracking works as expected. The tracking is relatively
cheap in terms of computational complexity. While the tracking has no limits on the maximum number
of people present, the localisation gets increasingly difficult with a complexity of O (n^n). Detecting
the correct peaks is a non-trivial problem because of multi-path reflections. In combination with UWB
radar detections, single and dual person tracking in a room is achieved. More people can be handled by
the tracking algorithm, which is detection-method-agnostic, but not by the localisation. There is some
room for improvement in the dual and triple-person case. However, going further than this is currently
unfeasible, because of the many reflections that occur. Furthermore, the large amount of possible person
locations also has an effect. This is a problem that scales with O (n^n) where n is the amount of
targets. ...
For both security and analytics, much research has gone into person tracking already. As a result, many
different state of the art technologies exist. However, in darkness or without a direct line of sight, much
less technologies are capable of this. The choices become especially limited when the setup needs to
be portable.
A method for person localisation and tracking is implemented. This method consists of a localisation
part, which works with any range-based detection method. Least square estimation is used to determine
the location from the radar detections. With two or more people, it is mathematically impossible to
distinguish which locations are correct, if only the current measurement is taken into account.
Thus, the first problem to be solved is connecting ranges to targets. This is done using target association.
After this is done, one-dimensional tracking can track people at lower computational cost.
The tracking is both in one dimension (per-radar) and in two dimensions. The Hungarian algorithm
is used for keeping track of people using a Kalman filter. The Kalman filter considers the predicted
next location and the measured next location, and makes a best guess. A neural network was used for
the optimisation of location-specific noise parameters, something that has not been done before in this
context. Single person tracking and two person tracking works as expected. The tracking is relatively
cheap in terms of computational complexity. While the tracking has no limits on the maximum number
of people present, the localisation gets increasingly difficult with a complexity of O (n^n). Detecting
the correct peaks is a non-trivial problem because of multi-path reflections. In combination with UWB
radar detections, single and dual person tracking in a room is achieved. More people can be handled by
the tracking algorithm, which is detection-method-agnostic, but not by the localisation. There is some
room for improvement in the dual and triple-person case. However, going further than this is currently
unfeasible, because of the many reflections that occur. Furthermore, the large amount of possible person
locations also has an effect. This is a problem that scales with O (n^n) where n is the amount of
targets.
different state of the art technologies exist. However, in darkness or without a direct line of sight, much
less technologies are capable of this. The choices become especially limited when the setup needs to
be portable.
A method for person localisation and tracking is implemented. This method consists of a localisation
part, which works with any range-based detection method. Least square estimation is used to determine
the location from the radar detections. With two or more people, it is mathematically impossible to
distinguish which locations are correct, if only the current measurement is taken into account.
Thus, the first problem to be solved is connecting ranges to targets. This is done using target association.
After this is done, one-dimensional tracking can track people at lower computational cost.
The tracking is both in one dimension (per-radar) and in two dimensions. The Hungarian algorithm
is used for keeping track of people using a Kalman filter. The Kalman filter considers the predicted
next location and the measured next location, and makes a best guess. A neural network was used for
the optimisation of location-specific noise parameters, something that has not been done before in this
context. Single person tracking and two person tracking works as expected. The tracking is relatively
cheap in terms of computational complexity. While the tracking has no limits on the maximum number
of people present, the localisation gets increasingly difficult with a complexity of O (n^n). Detecting
the correct peaks is a non-trivial problem because of multi-path reflections. In combination with UWB
radar detections, single and dual person tracking in a room is achieved. More people can be handled by
the tracking algorithm, which is detection-method-agnostic, but not by the localisation. There is some
room for improvement in the dual and triple-person case. However, going further than this is currently
unfeasible, because of the many reflections that occur. Furthermore, the large amount of possible person
locations also has an effect. This is a problem that scales with O (n^n) where n is the amount of
targets.