Microsystem for Improving Energy Efficiency by Minimizing Room-Level Greenhouse Effects in Homes
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors- Clarification on the definition and modeling of f(m). The manuscript models the room condition factor (f(m)) as a product of f(w) and f(t), yet later it is used directly as a DC voltage in simulations. Please clarify the process through which the abstract functional relationship is translated into concrete voltage values for circuit modeling.
- Sensor spectral response and justification for LDR Use, given the relatively slow response time and limited spectral selectivity of LDRs, why was the ORP12 CdS LDR selected over faster photodiodes or phototransistors? How do you mitigate potential issues in scenarios with rapidly fluctuating light levels?
- Modeling validity of RGHE estimation from SPDS output. The estimation model for RGHE based on SPDS voltage appears to be empirically derived. Have these relationships been validated experimentally in real-world conditions, or are they entirely simulation-based?
- Peak detector diode drop compensation, the SPDS introduces a significant 0.6 V drop due to the diode's forward voltage. Why wasn't a Schottky diode or a buffered peak detection circuit used to reduce this error, especially given the low input signal levels?
- Power consumption considerations, the manuscript lacks any discussion on power consumption for each stage of the microsystem. Please provide a breakdown of the current and power requirements for SCS, AS, and SPDS, and discuss how this aligns with the objective of energy efficiency.
- Ambiguity in Figure 3 axes. The time axis units are not clearly defined in Figure 3. Are the waveforms presented in actual microsecond time scales or are they symbolic simulation intervals?
- Clarification on diode behavior in Figure 6(b). The waveform shows a constant offset of 0.6 V. Is this forward voltage drop verified to be constant across all signal amplitudes? If not, provide a characterization of the variation.
- Need for clear legends and axes in Figure 8. The waveform outputs in Figure 8 use color-coding without clear legends or numerical scaling. Please add explicit legends and voltage/time scales for each signal.
- What is the role of Figure 10? Figure 10 is referenced without contextual explanation. What data does it represent and how does it support the manuscript's conclusions?
- Consistency of SPDS output in Table 2. Although SPDS output should be independent of f(m), small variations are still visible across f(m) values. Can you quantify and explain these discrepancies?
- Justification for 60 µs Stabilization Time. Given that sunlight intensity typically changes over seconds or minutes, why is the system optimized for a 60 µs response time? Would this choice lead to unnecessary power overhead?
- Lack of experimental validation. All results appear to be simulation-based using Cadence Virtuoso. Is there any fabricated layout or PCB prototype? If not, how are process variations and real-world parasitics accounted for?
- The manuscript asserts scalability but does not detail in which dimensions (e.g., number of rooms, sensors, analog-to-digital integration). Please elaborate on the scalability aspects.
- Technology node suitability: TSMC 180 nm is a mature node. Have other technology nodes (e.g., 90 nm, 65 nm) been considered? How does this choice impact area, power, and noise performance?
Author Response
We thank the reviewers for their valuable feedback. Their insights have significantly improved the clarity and technical depth of the manuscript. Below, we provide detailed responses to each comment.
Response to Reviewer 1:
Comment 1: clarification on the definition and modeling of f(m). The manuscript models the room condition factor (f(m)) as a product of f(w) and f(t), yet later it is used directly as a DC voltage in simulations. Please clarify the process through which the abstract functional relationship is translated into concrete voltage values for circuit modeling.
Response 1: In the revised manuscript, we clarified that f(m) is not a product but a composite function aggregating room-specific static factors such as window area and drape transparency. Additional attributes such as wall reflectivity and other architectural characteristics can also be incorporated, as these remain constant for a given room and are unaffected by dynamic sunlight variation. Therefore, the combined influence of these static factors is modeled as a single DC voltage level in circuit simulations. A low-pass RC filter in the auto-calibration circuit as shown in the figure (1) extracts the static component f(m) by allowing only low-frequency or DC signals to pass while attenuating dynamic changes like f(r) The capacitor smooths out rapid fluctuations, enabling the system to reference f(m) for accurate self-calibration continuously.
Comment 2: Sensor spectral response and justification for LDR Use, given the relatively slow response time and limited spectral selectivity of LDRs, why was the ORP12 CdS LDR selected over faster photodiodes or phototransistors? How do you mitigate potential issues in scenarios with rapidly fluctuating light levels?
Response 2: Although LDRs have a slower response time than photodiodes or phototransistors, our application targets slowly varying ambient light conditions; therefore, this delay is acceptable and does not impact performance. As the reviewer suggested, photodiodes or phototransistors could also be used, given their faster response time and broader spectral sensitivity. For rapid fluctuations, the system includes an SPDS stage with an appropriate RC time constant to filter out fluctuations, ensuring stable peak detection.
Comment 3: Modeling validity of RGHE estimation from SPDS output. The estimation model for RGHE based on SPDS voltage appears empirically derived. Have these relationships been validated experimentally in real-world conditions or are they entirely simulation-based?
Response 3: The RGHE estimation model was validated through pre-layout simulations, including temperature variation, and post-layout checks such as DRC, LVS, and ERC.
Comment 4: Peak detector diode drop compensation, the SPDS introduces a significant 0.6 V drop due to the diode's forward voltage. Why wasn't a Schottky diode or a buffered peak detection circuit used to reduce this error, especially given the low input signal levels?
Response 4: Your suggestion is well-founded. A Schottky diode with a lower forward voltage drops (~0.2–0.3 V) or an op-amp-buffered peak detector could minimize this drop and improve accuracy, particularly for low-amplitude signals. This enhancement is being considered for future iterations to further refine the system’s precision. The revised manuscript has added a note on this limitation and potential improvement.
Comment 5: Power consumption considerations, the manuscript lacks any discussion on power consumption for each stage of the microsystem. Please provide a breakdown of the current and power requirements for SCS, AS, and SPDS, and discuss how this aligns with the objective of energy efficiency.
Response 5: The SC consumed approximately 116.89 µW, while the AS, comprising the gain amp and resistor divider, drew around 204.94 µW. The SPDS, built using a diode-capacitor configuration with an RC holding network, consumed roughly 50.16 µW during dynamic signal tracking. In total, the power consumption across these three analog subsystems is approximately 370 µW, supporting the design’s practicality and suitability for low-power, real-time analog signal processing applications.
Comment 6: Ambiguity in Figure 3 axes. The time axis units are not clearly defined in Figure 3. Are the waveforms presented in actual microsecond time scales or are they symbolic simulation intervals?
Response 6: The waveform simulations were performed using Cadence Virtuoso under a 3.3 V supply, and the time units are in microseconds (µs). We have updated the figure caption accordingly to remove any ambiguity.
Comment 7: Clarification on diode behavior in Figure 6(b). The waveform shows a constant offset of 0.6 V. Is this forward voltage drop verified to be constant across all signal amplitudes? If not, provide a characterization of the variation.
Response 7: Yes, the observed 0.6 V offset corresponds to the forward voltage drop of the silicon diode used in the SPDS circuit. The forward voltage drop remains consistent across all simulations with different signal amplitudes, with only minimal variation (±0.02 V).
Comment 8: Need for clear legends and axes in Figure 8. The waveform outputs in Figure 8 use color-coding without clear legends or numerical scaling. Please add explicit legends and voltage/time scales for each signal.
Response 8: We have added clear legends identifying each waveform. All the x-axes represent time in microseconds, and the y-axes represent voltage in volts.
Comment 9: What is the role of Figure 10? Figure 10 is referenced without contextual explanation. What data does it represent and how does it support the manuscript's conclusions?
Response 9: Figure 10 presents the graphical correlation between SPDS output voltages and varying f(m) conditions across different f(r) inputs. It demonstrates that SPDS peak values remain consistent across varying static room conditions. And the graph allows us to give an equation for the lower and upper baselines.
Comment 10: Consistency of SPDS output in Table 2. Although SPDS output should be independent of f(m), small variations are still visible across f(m) values. Can you quantify and explain these discrepancies?
Response 10: Minor variations in SPDS output across different f(m) inputs are attributed to diode threshold non-idealities and RC time constant mismatches. These discrepancies are minimal in slow-changing applications like RGHE, where signal transitions are gradual. Future work will investigate whether these variations can be further reduced using Schottky diodes (with lower forward voltage) or replacing the diode-capacitor stage with an op-amp-based peak detector for enhanced precision.
Comment 11: Justification for 60 µs Stabilization Time. Given that sunlight intensity typically changes over seconds or minutes, why is the system optimized for a 60 µs response time? Would this choice lead to unnecessary power overhead?
Response 11: Although a 60 µs stabilization time may seem unnecessarily fast for tracking gradual sunlight changes, it enables the system to respond promptly to unexpected transients, such as sudden shading or artificial light flicker. This faster response does not introduce significant power overhead, as the total system power remains below 370 µW. Thus, the chosen response time effectively balances speed and energy efficiency for the intended application.
Comment 12: Lack of experimental validation. All results appear to be simulation-based using Cadence Virtuoso. Is there any fabricated layout or PCB prototype? If not, how are process variations and real-world parasitic accounted for?
Response 12: The layout of the RGHE microsystem was submitted for fabrication through MUSE and packaged using a QP-QFN32 (5 mm × 5 mm) package. A picture of the packaged chip is included in the manuscript, and real-time hardware testing will be addressed as part of future work.
Comment 13: The manuscript asserts scalability but does not detail in which dimensions (e.g., number of rooms, sensors, analog-to-digital integration). Please elaborate on the scalability aspects.
Response 13: The scalability asserted in the manuscript refers to the system's scalability to different CMOS technology nodes, enabling trade-offs between power, area, and performance. For example, while the current design uses the TSMC 180 nm node for its analog performance and ease of prototyping, the same system could be migrated to more advanced nodes (e.g., 130 nm or 65 nm). This can be easily integrated with many pin modules in multisensory systems. We have updated the manuscript included with this statement.
Comment 14: Technology node suitability: TSMC 180 nm is mature. Have other technology nodes (e.g., 90 nm, 65 nm) been considered? How does this choice impact area, power, and noise performance?
Response 14: The prototype can be designed using smaller technology nodes, which may reduce chip area and power consumption. However, for academic prototyping and ease of design and testing, the 180 nm node was selected. In future work, we plan to explore more advanced nodes for better integration and lower power in compact applications.
Reviewer 2 Report
Comments and Suggestions for Authorsthe authors present an innovative study on the design and simulation-based evaluation of a fully analog microsystem intended to improve energy efficiency in homes by monitoring and potentially mitigating the Room-Level Greenhouse Effect (RGHE). The research proposes a novel three-stage analog architecture (SCS, AS, SPDS) designed to isolate the dynamic Sunlight Intensity Radiation factor (f(r)) from static Room Condition Factors (f(m)) for real-time RGHE assessment. The manuscript details the circuit design methodology and verifies its functionality through simulations in Cadence Virtuoso using the TSMC technology library. The simulation findings suggest that the proposed microsystem can successfully isolate, amplify, and capture the peak value of the target dynamic signal with stabilization achieved within 60µs. The manuscript is well-structured and offers a potentially valuable analog approach to localized thermal monitoring in smart building environments. Therefore, I recommend acceptance of this manuscript after the authors address the following points to enhance its rigor and practical relevance:
The study's conclusions are currently based solely on simulation results performed in Cadence Virtuoso. While simulations are crucial for initial design verification, they lack validation through physical prototype testing in a real-world environment. Factors such as component tolerances, physical noise sources, process variations, and environmental influences (e.g., temperature fluctuations affecting analog components) are not fully captured in simulation but can significantly impact performance. It is recommended that future work include the fabrication and experimental testing of a physical prototype under varying light conditions and representative room environments to confirm the simulation results and assess the system's real-world robustness.
The study does not provide data on the long-term performance or durability of the proposed microsystem in a practical deployment scenario. The Light Dependent Resistor (LDR) used as the sensor is known to have potential limitations regarding response time, stability over temperature, aging effects, and susceptibility to environmental factors like dust accumulation, which are not evaluated in the short-term simulation context. Long-term field testing is necessary to assess the system's reliability, stability, potential calibration drift over time, and overall suitability for continuous monitoring applications.
While quantitative data (voltages, timings) are presented from simulations, the study relies heavily on this idealized data, particularly for developing the polynomial model mapping the SPDS output voltage to RGHE intensity. The validity of this model needs confirmation through empirical measurements from a physical system. Furthermore, inherent analog circuit noise characteristics and their impact on detection accuracy might not be fully represented in the simulations presented and should be evaluated experimentally.
Although an LDR is employed, the manuscript provides limited discussion on its practical limitations (e.g., slower response time compared to solar intensity fluctuations, potential non-linearity, unit-to-unit variability) in the context of this specific application. A comparative discussion considering alternative photodetectors (e.g., photodiodes, phototransistors) that might offer advantages in speed, stability, or linearity is missing. Additionally, the practical implementation and robustness of the mentioned "auto-calibration phase" require more detailed elaboration.
The manuscript focuses primarily on the microsystem's circuit-level design and simulation. There is inadequate discussion regarding the practical challenges of integrating this analog microsystem into existing smart home or Building Management Systems (BMS). Key aspects such as interface requirements (analog-to-digital conversion needs), overall system power consumption, unit cost, potential interference from artificial lighting sources, and the system's scalability for deployment in multiple rooms or buildings are not sufficiently explored. Addressing these practical considerations is crucial for assessing the technology's real-world applicability and potential for adoption.
Author Response
We thank the reviewers for their valuable feedback. Their insights have significantly improved the clarity and technical depth of the manuscript. Below, we provide detailed responses to each comment.
Response to Reviewer 2:
Comment 1: The study's conclusions are currently based solely on simulation results performed in Cadence Virtuoso. While simulations are crucial for initial design verification, they lack validation through physical prototype testing in a real-world environment. Factors such as component tolerances, physical noise sources, process variations, and environmental influences (e.g., temperature fluctuations affecting analog components) are not fully captured in simulation but can significantly impact performance. It is recommended that future work include the fabrication and experimental testing of a physical prototype under varying light conditions and representative room environments to confirm the simulation results and assess the system's real-world robustness.
Response 1: Thank you for the suggestion. These considerations have been addressed in the Future Work section of the manuscript.
Comment 2: The study does not provide data on the long-term performance or durability of the proposed microsystem in a practical deployment scenario. The Light Dependent Resistor (LDR) used as the sensor is known to have potential limitations regarding response time, stability over temperature, aging effects, and susceptibility to environmental factors like dust accumulation, which are not evaluated in the short-term simulation context. Long-term field testing is necessary to assess the system's reliability, stability, potential calibration drift over time, and overall suitability for continuous monitoring applications.
Response 2: The concerns regarding long-term reliability, sensor aging, environmental sensitivity, and calibration drift are valid and have now been acknowledged in the Future Work section of the manuscript. We propose conducting extended field testing and aging analysis of the LDR-based microsystem under varied environmental conditions to evaluate its durability and stability for continuous RGHE monitoring.
Additionally, we have included a plan to investigate the use of alternative light sensors such as photodiodes and phototransistors. These offer faster response times and improved spectral sensitivity, and their evaluation will allow us to assess performance trade-offs and identify opportunities for enhancing the system’s responsiveness and accuracy in future iterations.
Comment 3: While quantitative data (voltages, timings) are presented from simulations, the study relies heavily on this idealized data, particularly for developing the polynomial model mapping the SPDS output voltage to RGHE intensity. The validity of this model needs confirmation through empirical measurements from a physical system. Furthermore, inherent analog circuit noise characteristics and their impact on detection accuracy might not be fully represented in the simulations presented and should be evaluated experimentally.
Response 3: This concern has been addressed in the revised manuscript by expanding the Future Work section. We acknowledge that the current polynomial model and detection accuracy are based on idealized simulation results, and real-world validation is essential. As part of future efforts, we plan to validate the SPDS-to-RGHE mapping model through experimental measurements using a fabricated prototype.
Comment 4: Although an LDR is employed, the manuscript provides limited discussion on its practical limitations (e.g., slower response time compared to solar intensity fluctuations, potential non-linearity, unit-to-unit variability) in the context of this specific application. A comparative discussion considering alternative photodetectors (e.g., photodiodes, phototransistors) that might offer advantages in speed, stability, or linearity is missing. Additionally, the practical implementation and robustness of the mentioned "auto-calibration phase" require more detailed elaboration.
Response 4: Photodiodes and phototransistors offer faster response times, better linearity, and more consistent manufacturing characteristics. These sensors are particularly well-suited for applications involving rapid light fluctuations or critical light quantification. In future work, we plan to evaluate photodiode- and phototransistor-based implementations and compare their performance against the current LDR-based system, particularly in terms of response time, power efficiency, and RGHE detection accuracy under dynamic lighting conditions.
For auto calibration part: In the revised manuscript, we clarified that, where f(m) is not a product but a composite function that aggregates room-specific static factors such as window area and drape transparency. Additional attributes such as wall reflectivity and other architectural characteristics can also be incorporated, as these remain constant for a given room and are unaffected by dynamic sunlight variation. Therefore, the combined influence of these static factors is modeled as a single DC voltage level in circuit simulations. A low-pass RC filter in the auto-calibration circuit as shown in the figure (1) extracts the static component f(m) by allowing only low-frequency or DC signals to pass while attenuating dynamic changes like f(r) The capacitor smooths out rapid fluctuations, enabling the system to continuously reference f(m) for accurate self-calibration. An on-chip calibration circuit will be included as part of future work.
Comment 5: The manuscript focuses primarily on the microsystem's circuit-level design and simulation. There is inadequate discussion regarding the practical challenges of integrating this analog microsystem into existing smart home or Building Management Systems (BMS). Key aspects such as interface requirements (analog-to-digital conversion needs), overall system power consumption, unit cost, potential interference from artificial lighting sources, and the system's scalability for deployment in multiple rooms or buildings are not sufficiently explored. Addressing these practical considerations is crucial for assessing the technology's real-world applicability and potential for adoption.
Response 5:
The SC consumed approximately 116.89 µW, while the AS, comprising the gain amp and resistor divider, drew around 204.94 µW. The SPDS, built using a diode-capacitor configuration with an RC holding network, consumed roughly 50.16 µW during dynamic signal tracking. The power consumption across these three analog subsystems is approximately 370 µW, supporting the design’s practicality and suitability for low-power, real-time analog signal processing applications. Once initial testing and validation of the chip are complete, the unit cost can be significantly reduced through bulk production. With an estimated price point of approximately $1.50 per chip in high-volume fabrication runs. Potential interference from artificial lighting sources will be considered as part of future work, focusing on improving signal integrity through filtering.
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe clarifications provided are sufficient to be published.
Reviewer 2 Report
Comments and Suggestions for AuthorsI recommed accept
