A Look at Low-Noise Amplifiers

Maurizio Di Paolo Emilio, who has a PhD in Physics, is an Italian telecommunications engineer who works mainly as a software developer with a focus on data acquisition systems. Emilio has authored articles about electronic designs, data acquisition systems, power supplies, and photovoltaic systems. In this article, he provides an overview of what is generally available in low-noise amplifiers (LNAs) and some of the applications.

By Maurizio Di Paolo Emilio
An LNA, or preamplifier, is an electronic amplifier used to amplify sometimes very weak signals. To minimize signal power loss, it is usually located close to the signal source (antenna or sensor). An LNA is ideal for many applications including low-temperature measurements, optical detection, and audio engineering. This article presents LNA systems and ICs.

Signal amplifiers are electronic devices that can amplify a relatively small signal from a sensor (e.g., temperature sensors and magnetic-field sensors). The parameters that describe an amplifier’s quality are:

  • Gain: The ratio between output and input power or amplitude, usually measured in decibels
  • Bandwidth: The range of frequencies in which the amplifier works correctly
  • Noise: The noise level introduced in the amplification process
  • Slew rate: The maximum rate of voltage change per unit of time
  • Overshoot: The tendency of the output to swing beyond its final value before settling down

Feedback amplifiers combine the output and input so a negative feedback opposes the original signal (see Figure 1). Feedback in amplifiers provides better performance. In particular, it increases amplification stability, reduces distortion, and increases the amplifier’s bandwidth.

 Figure 1: A feedback amplifier model is shown here.


Figure 1: A feedback amplifier model is shown.

A preamplifier amplifies an analog signal, generally in the stage that precedes a higher-power amplifier.

IC LOW-NOISE PREAMPLIFIERS
Op-amps are widely used as AC amplifiers. Linear Technology’s LT1028 or LT1128 and Analog Devices’s ADA4898 or AD8597 are especially suitable ultra-low-noise amplifiers. The LT1128 is an ultra-low-noise, high-speed op-amp. Its main characteristics are:

  • Noise voltage: 0.85 nV/√Hz at 1 kHz
  • Bandwidth: 13 MHz
  • Slew rate: 5 V/µs
  • Offset voltage: 40 µV

Both the Linear Technology and Analog Devices amplifiers have voltage noise density at 1 kHz at around 1 nV/√Hz  and also offer excellent DC precision. Texas Instruments (TI)  offers some very low-noise amplifiers. They include the OPA211, which has 1.1 nV/√Hz  noise density at a  3.6 mA from 5 V supply current and the LME49990, which has very low distortion. Maxim Integrated offers the MAX9632 with noise below 1nV/√Hz.

The op-amp can be realized with a bipolar junction transistor (BJT), as in the case of the LT1128, or a MOSFET, which works at higher frequencies and with a higher input impedance and a lower energy consumption. The differential structure is used in applications where it is necessary to eliminate the undesired common components to the two inputs. Because of this, low-frequency and DC common-mode signals (e.g., thermal drift) are eliminated at the output. A differential gain can be defined as (Ad = A2 – A1) and a common-mode gain can be defined as (Ac = A1 + A2 = 2).

An important parameter is the common-mode rejection ratio (CMRR), which is the ratio of common-mode gain to the differential-mode gain. This parameter is used to measure the  differential amplifier’s performance.

Figure 2: The design of a simple preamplifier is shown. Its main components are the Linear Technology LT112 and the Interfet IF3602 junction field-effect transistor (JFET).

Figure 2: The design of a simple preamplifier is shown. Its main components are the Linear Technology LT1128 and the Interfet IF3602 junction field-effect transistor (JFET).

Figure 2 shows a simple preamplifier’s design with 0.8 nV/√Hz at 1 kHz background noise. Its main components are the LT1128 and the Interfet IF3602 junction field-effect transistor (JFET).  The IF3602 is a dual N-channel JFET used as stage for the op-amp’s input. Figure 3 shows the gain and Figure 4 shows the noise response.

Figure 3: The gain of a low-noise preamplifier.

Figure 3: The is a low-noise preamplifier’s gain.

 

Figure 4: The noise response of a low-noise preamplifier

Figure 4: A low-noise preamplifier’s noise response is shown.

LOW NOISE PREAMPLIFIER SYSTEMS
The Stanford Research Systems SR560 low-noise voltage preamplifier has a differential front end with 4nV/√Hz input noise and a 100-MΩ input impedance (see Photo 1a). Input offset nulling is accomplished by a front-panel potentiometer, which is accessible with a small screwdriver. In addition to the signal inputs, a rear-panel TTL blanking input enables you to quickly turn the instrument’s gain on and off (see Photo 1b).

Photo 1a:The Stanford Research Systems SR560 low-noise voltage preamplifier

Photo 1a: The Stanford Research Systems SR560 low-noise voltage preamplifier. (Photo courtesy of Stanford Research Systems)

Photo 1 b: A rear-panel TTL blanking input enables you to quickly turn the Stanford Research Systems SR560 gain on and off.

Photo 1b: A rear-panel TTL blanking input enables you to quickly turn the Stanford Research Systems SR560 gain on and off. (Photo courtesy of Stanford Research Systems)

The Picotest J2180A low-noise preamplifier provides a fixed 20-dB gain while converting a 1-MΩ input impedance to a 50-Ω output impedance and 0.1-Hz to 100-MHz bandwidth (see Photo 2). The preamplifier is used to improve the sensitivity of oscilloscopes, network analyzers, and spectrum analyzers while reducing the effective noise floor and spurious response.

Photo 2: The Picotest J2180A low-noise preamplifier is shown.

Photo 2: The Picotest J2180A low-noise preamplifier is shown. (Photo courtesy of picotest.com)

Signal Recovery’s Model 5113 is among the best low-noise preamplifier systems. Its principal characteristics are:

  • Single-ended or differential input modes
  • DC to 1-MHz frequency response
  • Optional low-pass, band-pass, or high-pass signal channel filtering
  • Sleep mode to eliminate digital noise
  • Optically isolated RS-232 control interface
  • Battery or line power

The 5113 (see Photo 3 and Figure 5) is used in applications as diverse as radio astronomy, audiometry, test and measurement, process control, and general-purpose signal amplification. It’s also ideally suited to work with a range of lock-in amplifiers.

Photo 3: This is the Signal Recovery Model 5113 low-noise pre-amplifier.

Photo 3: This is the Signal Recovery Model 5113 low-noise preamplifier. (Photo courtesy of Signal Recovery)

Figure 5: Noise contour figures are shown for the Signal Recovery Model 5113.

Figure 5: Noise contour figures are shown for the Signal Recovery Model 5113.

WRAPPING UP
This article briefly introduced low-noise amplifiers, in particular IC system designs utilized in simple or more complex systems such as the Signal Recovery Model 5113, which is a classic amplifier able to obtain different frequency bands with relative gain. A similar device is the SR560, which is a high-performance, low-noise preamplifier that is ideal for a wide variety of applications including low-temperature measurements, optical detection, and audio engineering.

Moreover, the Krohn-Hite custom Models 7000 and 7008 low-noise differential preamplifiers provide a high gain amplification to 1 MHz with an AC output derived from a very-low-noise FET instrumentation amplifier.

One common LNA amplifier is a satellite communications system. The ground station receiving antenna will connect to an LNA, which is needed because the received signal is weak. The received signal is usually a little above background noise. Satellites have limited power, so they use low-power transmitters.

Telecommunications engineer Maurizio Di Paolo Emilio was born in Pescara, Italy. Working mainly as a software developer with a focus on data acquisition systems, he helped design the thermal compensation system (TCS) for the optical system used in the Virgo Experiment (an experiment for detecting gravitational waves). Maurizio currently collaborates with researchers at the University of L’Aquila on X-ray technology. He also develops data acquisition hardware and software for industrial applications and manages technical training courses. To learn more about Maurizio and his expertise, read his essay on “The Future of Data Acquisition Technology.”

Using Socially Assistive Robots to Address the Caregiver Gap

David Feil-Seifer

Editor’s Note: David Feil-Seifer, a Postdoctoral Fellow in the Computer Science Department at Yale University, wrote this  essay for Circuit Cellar. Feil-Seifer focuses his research on socially assistive robotics (SAR), particularly the study of human-robot interaction for children with autism spectrum disorders (ASD). His dissertation work addressed autonomous robot behavior so that socially assistive robots can recognize and respond to a child’s behavior in unstructured play. He recently was hired as Assistant Professor of Computer Science at the University of Nevada, Reno.

There are looming health care and education crises on the horizon. Baby boomers are getting older and requiring more care, which puts pressure on caregivers. The US nursing shortage is projected to worsen. Similarly, the rapid growth of diagnoses of developmental disorders suggests a greater need for educators, one that the education system is struggling to meet. These great and growing shortfalls in the number of caregivers and educators may be addressed (in part) through the use of socially assistive robotics.

In health care, non-contact repetitive tasks make up a large part of a caregiver’s day. Tasks such as monitoring instruments only require a check to verify that readings are within norms. By offloading these tasks to an automated system, a nurse or doctor could spend more time doing work that better leverages their medical training. A robot can effectively perform simple repetitive tasks (e.g., monitoring breath spirometry exercises or post-stroke rehabilitation compliance).

I coined the term “socially assistive robotics” (SAR) to describe robots that provide such assistance through social rather than physical interaction. My research is the development of SAR algorithms and complete systems relevant to domains such as post-stroke rehabilitation, elder care, and therapeutic interaction for children with autism spectrum disorders (ASD). A key challenge for such autonomous SAR systems is the ability to sense, interpret, and properly respond to human social behavior.

One of my research priorities is developing a socially assistive robotic system for children with ASD. Children with ASD are characterized by social impairments, communication difficulties, and repetitive and stereotyped behaviors. Significant anecdotal evidence indicates that some children with ASD respond socially to robots, which could have therapeutic ramifications. We envision a robot that could act as a catalyst for social interaction, both human-robot and human-human, thus aiding ASD users’ human-human socialization. In such a scenario, the robot is not specifically generating social behavior or participating in social interaction, but instead behaves in a way known to provoke human-human interaction.

David Feil-Seifer developed an autonomous robot that recognizes and appropriately responds to a child’s free-form behavior in play contexts, similar to those seen in some more traditional autism spectrum disorder (ASD) therapies.

Enabling a robot to exhibit and understand social behavior with a child is challenging. Children are highly individual and thus technology used for social interaction needs to be robust to be effective. I developed an autonomous robot that recognizes and appropriately responds to a child’s free-form behavior in play contexts, similar to those seen in some more traditional ASD therapies.

To detect and mitigate child distress, I developed a methodology for learning and then applying a data-driven spatiotemporal model of social behavior based on distance-based features to automatically differentiate between typical vs. aversive child-robot interactions. Using a Gaussian mixture model learned over distance-based feature data, the developed system was able to detect and interpret social behavior with sufficient accuracy to recognize child distress. The robot can use this to change its own behavior to encourage positive social interaction.

To encourage human-human interaction once human-robot interaction was achieved, I developed a navigation planner that used the above spatiotemporal model. This was used to maintain the robot’s spatial relationship with a child to sustain interaction while also guiding the child to a particular location in a room. This could be used to encourage a child to move toward another interaction partner (e.g., a parent). The desired spatial interaction behavior is achieved by modifying an established trajectory planner to weigh candidate trajectories based on conformity to a trained model of the desired behavior.

I also developed a methodology for robot behavior that provides autonomous feedback for a robot-child imitation and turn-taking game. This was accomplished by incorporating an established therapeutic model of feedback along with a trained model of imitation behavior. This is used as part of an autonomous system that can play Simon Says, recognize when the rules have been violated, and provide appropriate feedback.

A growing body of data supports the hypothesis that robots have the potential to aid in addressing the needs of people through non-contact assistance. My research, along with that of many others, has resulted in technical advances for robots providing assistance to people. However, there is a long way to go before these systems can be deployed as a therapeutic platform. Given that the beneficiary populations are growing, and the required therapeutic needs are increasing far more rapidly than the existing resources to address it, SAR could provide lasting benefits to people in need.

David Feil-Seifer, a Postdoctoral Fellow in the Computer Science Department at Yale University, focuses his research on socially assistive robotics (SAR), particularly the study of human-robot interaction for children with autism spectrum disorders (ASD). His dissertation work addressed autonomous robot behavior so that socially assistive robots can recognize and respond to a child’s behavior in unstructured play. David received his MS and PhD in Computer Science from the University of Southern California and a BS in Computer Science from the University of Rochester, NY. He recently was hired as Assistant Professor of Computer Science at the University of Nevada, Reno.