Power Supply Rejection Ratio (PSRR) is the ratio of the change in supply voltage to the resulting change in output voltage, expressed in decibels (dB). It measures how effectively an amplifier, data converter, or regulator isolates its output from ripple, noise, and transient fluctuations present on its DC power rail. A higher PSRR indicates superior isolation.
Glossary
Power Supply Rejection Ratio (PSRR)

What is Power Supply Rejection Ratio (PSRR)?
Power Supply Rejection Ratio (PSRR) quantifies a circuit's ability to prevent power rail noise from corrupting its output signal, a critical parameter in RF fingerprinting where supply-induced modulation creates unique device signatures.
In RF fingerprinting, poor PSRR becomes an exploitable feature. Supply noise—from switching regulators or digital circuitry—couples into the analog signal path, amplitude-modulating or phase-modulating the transmitted waveform. This environmentally-coupled, device-specific interaction between the power delivery network and the signal chain creates a unique, hardware-dependent signature distinct from static non-linearity.
Key Characteristics of PSRR in Fingerprinting
Power Supply Rejection Ratio (PSRR) quantifies a circuit's ability to prevent ripple and noise on its power rail from corrupting the output signal. In RF fingerprinting, poor PSRR is an exploitable feature, as it allows power supply variations to modulate the transmitted waveform, creating a unique, environmentally-coupled signature.
Definition and Mechanism
PSRR is the ratio of the change in supply voltage to the resulting change in the output signal, expressed in decibels (dB). A low PSRR means a significant portion of power supply noise couples directly into the signal path. This coupling occurs through finite output impedance of current sources and incomplete isolation in amplifier stages. The specific frequency response of PSRR is a device-unique trait, varying due to on-chip decoupling capacitance and parasitic inductances.
PSRR as a Fingerprinting Feature
A device's PSRR is not a perfect, flat line; it degrades at higher frequencies. This frequency-dependent attenuation curve is shaped by the physical layout and component values of the voltage regulation and bias networks. An attacker or authenticator can inject a known, low-amplitude pilot tone onto the power rail and measure its modulated remnant at the RF output. The amplitude and phase of this remnant, relative to the injected tone, form a unique hardware signature.
Environmental Coupling and Drift
Unlike static non-linearity, PSRR-based fingerprints are environmentally reactive. A device's power supply noise is a function of its activity (e.g., CPU load, transmit power) and external factors (e.g., battery voltage droop, temperature). This creates a dynamic signature that reflects the device's instantaneous operating state. While this complicates authentication, it also provides a rich, multi-dimensional feature space that is extremely difficult to clone or replay.
Key Metrics and Measurement
Characterizing PSRR for fingerprinting requires more than a single dB value. Critical metrics include:
- PSRR vs. Frequency: The signature is the entire curve, not a single point.
- Supply-Induced Jitter: Noise on the supply modulates the zero-crossings of clock buffers, creating a unique phase noise profile.
- Intermodulation with Signal: The mixing of supply noise with the intentional signal creates deterministic sidebands that are a direct product of the PSRR non-linearity.
Distinction from Other Impairments
PSRR is distinct from static non-linearity (INL/DNL) because it is a dynamic, stimulus-dependent impairment. While INL is a fixed deviation from an ideal transfer curve, PSRR dictates how a time-varying disturbance on the power rail maps to the output. It is also distinct from thermal noise, as PSRR-related artifacts are deterministic given a known supply fluctuation. This makes it a complementary feature for multi-modal fingerprinting systems.
Exploitation in DACs and ADCs
In a Digital-to-Analog Converter (DAC), poor PSRR on the reference voltage or output buffer supply directly amplitude-modulates the reconstructed waveform. In an Analog-to-Digital Converter (ADC), supply noise couples into the comparator and sample-and-hold circuits, causing gain errors and aperture jitter that are supply-dependent. Time-interleaved ADCs are particularly susceptible, as supply noise modulates the already-present interleaving mismatch spurs, creating a complex, coupled signature.
Frequently Asked Questions
Explore the critical role of Power Supply Rejection Ratio in RF fingerprinting, where a circuit's inability to perfectly reject power rail noise creates a unique, environmentally-coupled signature that can be exploited for device identification.
Power Supply Rejection Ratio (PSRR) is a measure of a circuit's ability to suppress ripple and noise present on its power supply rail from appearing at its output. It is defined as the ratio of the change in supply voltage to the resulting change in output voltage, typically expressed in decibels (dB). A higher PSRR indicates better isolation. In an operational amplifier or data converter, the internal bias circuits and amplifier stages rely on a clean, stable supply. When the supply rail carries ripple—from switching regulators, digital clock noise, or mains hum—finite PSRR means a fraction of this noise couples into the signal path. This coupling occurs through mechanisms like channel-length modulation in transistors, where drain-source voltage fluctuations directly modulate the bias current, or through parasitic capacitive paths between supply and signal nodes. The result is that the output signal becomes a function not only of the input but also of the power supply's instantaneous voltage, creating an environmentally-coupled modulation that is unique to each physical instance of a chip due to microscopic manufacturing variances in its power distribution network and decoupling elements.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Related Terms
Understanding PSRR requires a holistic view of the analog signal chain. The following concepts define the key non-idealities that, alongside power supply coupling, form a device's unique hardware fingerprint.
Process-Voltage-Temperature (PVT) Variation
The collective impact of manufacturing process shifts, supply voltage fluctuations, and operating temperature changes on circuit performance. PVT defines the statistical distribution of hardware impairments that make each device unique.
- Process: Microscopic variations in doping and lithography
- Voltage: IR drops and supply ripple coupling (directly linked to PSRR)
- Temperature: Thermal gradients affecting carrier mobility and threshold voltages
PSRR is itself a PVT-sensitive parameter, varying across the operational envelope and contributing to a dynamic, environmentally-coupled fingerprint.
Integral Non-Linearity (INL)
A measure of a data converter's static linearity, defined as the maximum deviation of the actual transfer function from an ideal straight line. INL creates a unique, process-dependent signature in the output waveform.
- Measured in Least Significant Bits (LSBs)
- Arises from component mismatch in DAC resistor ladders and ADC comparators
- A smooth, low-frequency INL profile is a strong, persistent identifier
When power supply ripple modulates the converter's reference voltage, the effective INL profile shifts dynamically, creating a supply-dependent distortion signature.
Differential Non-Linearity (DNL)
The deviation between an actual step width and the ideal 1 Least Significant Bit (LSB) step in a data converter. Large DNL errors can lead to missing codes—a permanent, highly distinctive gap in the transfer function.
- A DNL of -1 LSB indicates a missing code
- Caused by random comparator offsets and capacitor mismatch
- Missing codes are a binary, unclonable feature for device identification
Poor PSRR on internal bias generators can cause DNL to vary with supply ripple, making the missing code pattern a function of the power rail noise spectrum.
Aperture Jitter
The sample-to-sample variation in the precise instant a sample-and-hold circuit captures a signal. This timing uncertainty modulates the phase of the digitized waveform and creates a unique, clock-related fingerprint.
- Measured in femtoseconds or picoseconds RMS
- Translates directly to increased noise floor at higher input frequencies
- Jitter spectrum often contains deterministic spurs from coupled supply noise
A switching power supply with poor PSRR in the clock generation circuitry directly injects periodic jitter, creating supply-synchronous phase modulation sidebands that are highly identifiable.
Phase Noise
The frequency-domain representation of rapid, random fluctuations in a signal's phase, often originating from oscillator instabilities. Phase noise manifests as a unique spectral skirt around the carrier.
- Characterized by dBc/Hz at specific offset frequencies
- Close-in phase noise is dominated by flicker noise upconversion
- Far-out phase noise floor is set by thermal noise and supply-induced interference
Power supply ripple at the oscillator's voltage regulator input is directly converted to phase noise via the supply pushing mechanism, imprinting the PSRR profile onto the carrier's spectral purity.
Intermodulation Distortion (IMD)
Non-linear distortion products generated when two or more signals at different frequencies are applied to a non-linear system. IMD reveals the specific polynomial transfer function of the transmitter chain.
- Second-order (IM2) and third-order (IM3) products are most significant
- The amplitude and phase of IMD products are device-specific
- Power supply ripple can intermodulate with the signal, creating supply-borne IMD spurs
A power amplifier with poor PSRR will exhibit IMD products whose frequencies are the sum and difference of the signal and the power supply ripple frequency, a telltale signature of the power delivery network.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us