{"id":{"repo_id":"cork","oai_identifier":"oai:cora.ucc.ie:10468/17478"},"canonical_url":"https://search.dev.ndltd.org/etd/cork/oai:cora.ucc.ie:10468/17478","repository":{"repo_id":"cork","name":"University College Cork","base_url":"https://cora.ucc.ie/server/oai/request"},"display":{"title":"Bridge to digital converters for environmental monitoring in edge IoT devices","abstract":"Continuous air monitoring is crucial for maintaining safe and comfortable home and work environments, particularly in industries that handle flammable or toxic gases, which pose serious health risks. Consequently, there is an increasing focus on developing wearable gas sensors to protect workers in manufacturing and other sectors requiring constant exposure monitoring. This thesis explores interfaces for resistive gas sensors and discusses the distinct challenges in designing environmental monitoring edge IoT devices. The Wheatstone bridge (WhB), which is the conventional architecture for interfacing with resistive-based sensors, typically produces a mV-level differential signal that needs to be detected in a higher common-mode voltage. This presents particular challenges for ADCs digitising WhB outputs, commonly referred to as Bridge-to-Digital Converters (BDCs), which need to have high input impedance and gain, be linear, and have offset and noise levels similar to those of the WhB. This work proposes three bridge-to-digital conversion solutions to address challenges in resistive sensor-based monitoring systems. First, it introduces a successive approximation register (SAR) based bridge-to-digital converter (BDC) that utilizes an error-feedback modulator DAC, providing a low-area alternative to the traditional binary weighted approach. The BDC occupies 0.01 mm2 area, and consumes 7.4 μW with a 45 ms conversion time. It achieves 9.1 ENOB for a DC input and a Walden FoM of 0.65 nJ/conv-step. Secondly, a ramp-based BDC is detailed, employing the same DAC with non-uniform sampling to enhance both resolution and sampling frequency. The BDC occupies an area of 0.014 mm2. It achieves an SNR of 49.97 dB and 65.6 dB, for dynamic and static inputs, respectively. The BDC consumes 30 μW with a 0.41 ms conversion time, resulting in a Walden FoM of 0.048 nJ/Conv-step. Finally, a threshold detection-based BDC is presented, aimed at reducing power consumption by decreasing the volume of generated output data. This design occupies 0.012 mm2 with 10 bits resolution, consuming 4.8 μW of power while operating at a clock frequency of 20 MHz. All BDCs are designed using a 65-nm CMOS technology and offer a wide input range of 830 mV. Despite the unique design constraints associated with gas sensors, the methodologies discussed in this thesis have broader applications in the IoT field, where most devices must operate for prolonged periods without requiring battery replacement, while managing vast amounts of continuously collected data.","abstract_html":"Continuous air monitoring is crucial for maintaining safe and comfortable home and work environments, particularly in industries that handle flammable or toxic gases, which pose serious health risks. Consequently, there is an increasing focus on developing wearable gas sensors to protect workers in manufacturing and other sectors requiring constant exposure monitoring. This thesis explores interfaces for resistive gas sensors and discusses the distinct challenges in designing environmental monitoring edge IoT devices. The Wheatstone bridge (WhB), which is the conventional architecture for interfacing with resistive-based sensors, typically produces a mV-level differential signal that needs to be detected in a higher common-mode voltage. This presents particular challenges for ADCs digitising WhB outputs, commonly referred to as Bridge-to-Digital Converters (BDCs), which need to have high input impedance and gain, be linear, and have offset and noise levels similar to those of the WhB. This work proposes three bridge-to-digital conversion solutions to address challenges in resistive sensor-based monitoring systems. First, it introduces a successive approximation register (SAR) based bridge-to-digital converter (BDC) that utilizes an error-feedback modulator DAC, providing a low-area alternative to the traditional binary weighted approach. The BDC occupies 0.01 mm2 area, and consumes 7.4 μW with a 45 ms conversion time. It achieves 9.1 ENOB for a DC input and a Walden FoM of 0.65 nJ/conv-step. Secondly, a ramp-based BDC is detailed, employing the same DAC with non-uniform sampling to enhance both resolution and sampling frequency. The BDC occupies an area of 0.014 mm2. It achieves an SNR of 49.97 dB and 65.6 dB, for dynamic and static inputs, respectively. The BDC consumes 30 μW with a 0.41 ms conversion time, resulting in a Walden FoM of 0.048 nJ/Conv-step. Finally, a threshold detection-based BDC is presented, aimed at reducing power consumption by decreasing the volume of generated output data. This design occupies 0.012 mm2 with 10 bits resolution, consuming 4.8 μW of power while operating at a clock frequency of 20 MHz. All BDCs are designed using a 65-nm CMOS technology and offer a wide input range of 830 mV. Despite the unique design constraints associated with gas sensors, the methodologies discussed in this thesis have broader applications in the IoT field, where most devices must operate for prolonged periods without requiring battery replacement, while managing vast amounts of continuously collected data.","abstract_has_math":false,"creators":["Fordymacka, Annamaria"],"institution":"University College Cork","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["O'Connell, Ivan","McCarthy, Kevin G."],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T01:46:32Z","subjects":["Bridge-to-digital converter","Resistive sensors","Analog-to-information converter","Data converters","Threshold detection"],"languages":["en"],"rights":["© 2024, Annamaria Fordymacka."],"rights_urls":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10468/17478","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["O'Connell, Ivan","McCarthy, Kevin G."]},{"key":"dc:creator","label":"Author","values":["Fordymacka, Annamaria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-13T14:53:13Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-13T14:53:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["University College Cork"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD - Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bridge-to-digital converter","Resistive sensors","Analog-to-information converter","Data converters","Threshold detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2024, Annamaria Fordymacka."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10468/17478"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Continuous air monitoring is crucial for maintaining safe and comfortable home and work environments, particularly in industries that handle flammable or toxic gases, which pose serious health risks. Consequently, there is an increasing focus on developing wearable gas sensors to protect workers in manufacturing and other sectors requiring constant exposure monitoring. This thesis explores interfaces for resistive gas sensors and discusses the distinct challenges in designing environmental monitoring edge IoT devices. The Wheatstone bridge (WhB), which is the conventional architecture for interfacing with resistive-based sensors, typically produces a mV-level differential signal that needs to be detected in a higher common-mode voltage. This presents particular challenges for ADCs digitising WhB outputs, commonly referred to as Bridge-to-Digital Converters (BDCs), which need to have high input impedance and gain, be linear, and have offset and noise levels similar to those of the WhB. This work proposes three bridge-to-digital conversion solutions to address challenges in resistive sensor-based monitoring systems. First, it introduces a successive approximation register (SAR) based bridge-to-digital converter (BDC) that utilizes an error-feedback modulator DAC, providing a low-area alternative to the traditional binary weighted approach. The BDC occupies 0.01 mm2 area, and consumes 7.4 μW with a 45 ms conversion time. It achieves 9.1 ENOB for a DC input and a Walden FoM of 0.65 nJ/conv-step. Secondly, a ramp-based BDC is detailed, employing the same DAC with non-uniform sampling to enhance both resolution and sampling frequency. The BDC occupies an area of 0.014 mm2. It achieves an SNR of 49.97 dB and 65.6 dB, for dynamic and static inputs, respectively. The BDC consumes 30 μW with a 0.41 ms conversion time, resulting in a Walden FoM of 0.048 nJ/Conv-step. Finally, a threshold detection-based BDC is presented, aimed at reducing power consumption by decreasing the volume of generated output data. This design occupies 0.012 mm2 with 10 bits resolution, consuming 4.8 μW of power while operating at a clock frequency of 20 MHz. All BDCs are designed using a 65-nm CMOS technology and offer a wide input range of 830 mV. Despite the unique design constraints associated with gas sensors, the methodologies discussed in this thesis have broader applications in the IoT field, where most devices must operate for prolonged periods without requiring battery replacement, while managing vast amounts of continuously collected data."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bridge to digital converters for environmental monitoring in edge IoT devices"]}]}],"canonical_facts":{"dc:contributor.advisor":["O'Connell, Ivan","McCarthy, Kevin G."],"dc:creator":["Fordymacka, Annamaria"],"dc:date.accessioned":["2025-05-13T14:53:13Z"],"dc:date.available":["2025-05-13T14:53:13Z"],"dc:date.issued":["2024"],"dc:description.abstract":["Continuous air monitoring is crucial for maintaining safe and comfortable home and work environments, particularly in industries that handle flammable or toxic gases, which pose serious health risks. Consequently, there is an increasing focus on developing wearable gas sensors to protect workers in manufacturing and other sectors requiring constant exposure monitoring. This thesis explores interfaces for resistive gas sensors and discusses the distinct challenges in designing environmental monitoring edge IoT devices. The Wheatstone bridge (WhB), which is the conventional architecture for interfacing with resistive-based sensors, typically produces a mV-level differential signal that needs to be detected in a higher common-mode voltage. This presents particular challenges for ADCs digitising WhB outputs, commonly referred to as Bridge-to-Digital Converters (BDCs), which need to have high input impedance and gain, be linear, and have offset and noise levels similar to those of the WhB. This work proposes three bridge-to-digital conversion solutions to address challenges in resistive sensor-based monitoring systems. First, it introduces a successive approximation register (SAR) based bridge-to-digital converter (BDC) that utilizes an error-feedback modulator DAC, providing a low-area alternative to the traditional binary weighted approach. The BDC occupies 0.01 mm2 area, and consumes 7.4 μW with a 45 ms conversion time. It achieves 9.1 ENOB for a DC input and a Walden FoM of 0.65 nJ/conv-step. Secondly, a ramp-based BDC is detailed, employing the same DAC with non-uniform sampling to enhance both resolution and sampling frequency. The BDC occupies an area of 0.014 mm2. It achieves an SNR of 49.97 dB and 65.6 dB, for dynamic and static inputs, respectively. The BDC consumes 30 μW with a 0.41 ms conversion time, resulting in a Walden FoM of 0.048 nJ/Conv-step. Finally, a threshold detection-based BDC is presented, aimed at reducing power consumption by decreasing the volume of generated output data. This design occupies 0.012 mm2 with 10 bits resolution, consuming 4.8 μW of power while operating at a clock frequency of 20 MHz. All BDCs are designed using a 65-nm CMOS technology and offer a wide input range of 830 mV. Despite the unique design constraints associated with gas sensors, the methodologies discussed in this thesis have broader applications in the IoT field, where most devices must operate for prolonged periods without requiring battery replacement, while managing vast amounts of continuously collected data."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10468/17478"],"dc:language.iso":["en"],"dc:publisher":["University College Cork"],"dc:rights":["© 2024, Annamaria Fordymacka."],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Bridge-to-digital converter","Resistive sensors","Analog-to-information converter","Data converters","Threshold detection"],"dc:title":["Bridge to digital converters for environmental monitoring in edge IoT devices"],"dc:type":["Doctoral thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["PhD - Doctor of Philosophy"]},"updated_at":"2026-07-24T01:46:32Z"}