Question 1

Identify the ADC architecture — what type is the ADC0804? The classic ADC0804 (8-bit, microprocessor-compatible ADC) belongs to which analog-to-digital conversion architecture?

Accepted Answer

Introduction / Context: The ADC0804 is a well-known 8-bit ADC used in early microprocessor systems. Recognizing its architecture helps predict speed, interface timing, and use cases. Successive-approximation (SAR) converters like ADC0804 offer deterministic, moderate-speed conversions with simple parallel interfaces, making them popular in education and embedded designs. Given Data / Assumptions: ADC0804 resolution: 8 bits, microprocessor-friendly signals (WR, INTR, CS). Typical conversion times on the order of tens to hundreds of microseconds (with an external clock). No massive comparator array as in flash ADCs. Concept / Approach: SAR ADCs perform a binary search using an internal DAC and comparator. The ADC0804 cycles through bit trials from MSB to LSB, requiring a fixed number of steps. This matches the datasheet's timing diagrams and WR/INTR handshake. Single/dual-slope parts are common in instruments but have different timing behavior, and digital-ramp (counter-type) ADCs would require variable-time counting to the code value, which the ADC0804 does not do. Step-by-Step Solution: Check interface pins: SAR-style start (WR) and end-of-conversion (INTR) are present.Review timing: fixed number of comparator decisions per conversion.No evidence of multi-comparator flash array.Conclude: ADC0804 is a SAR ADC. Verification / Alternative check: Manufacturer datasheets explicitly label ADC0804 as a successive-approximation ADC with an internal DAC and comparator. Conversion time scales with the internal clock but not with input level. Why Other Options Are Wrong: Single/dual-slope: do not match the interface and timing of ADC0804. Digital ramp: would have input-dependent conversion time and different control logic. Flash: would require 2^8 − 1 comparators and yield much higher speed. Common Pitfalls: Confusing ADC0804 (SAR) with integrating ADCs used in multimeters (dual-slope). Final Answer: successive-approximation analog-to-digital converter

Question 2

R–2R ladder DAC (4-bit) — how many distinct resistor values are required? A 4-bit R–2R ladder digital-to-analog converter is built using only replicated values. How many different resistor values are used in the ladder network?

Accepted Answer

Introduction / Context: The R–2R ladder is a highly practical DAC topology because it uses only two resistor values, regardless of resolution. This greatly simplifies matching and scaling compared with binary-weighted DACs that require precise powers-of-two resistor ratios. Understanding this property explains why R–2R ladders are common in integrated DACs and lab circuits alike. Given Data / Assumptions: We consider a standard R–2R ladder DAC for 4 bits. Ideal resistors are perfectly matched. Switches connect each bit node either to reference or ground. Concept / Approach: In an R–2R ladder, the entire network is formed by repeating segments that contain only R and 2R. Each bit adds another rung of the ladder with the same two values, ensuring that the Thevenin equivalent seen by each bit is properly scaled. The number of bits changes the number of components but never the number of distinct values required. Step-by-Step Solution: Identify ladder elements → they alternate between R and 2R.Count unique values → only two (R and 2R).Resolution (4-bit here) adds rungs but does not add new values.Therefore, the answer is two resistor values. Verification / Alternative check: Textbook schematics confirm that any N-bit R–2R DAC repeats the same two values. SPICE simulations with R = 10 kΩ and 2R = 20 kΩ demonstrate the expected binary-weighted outputs for all 4-bit codes. Why Other Options Are Wrong: One value: impossible, as the ladder requires both R and 2R. Three/four values: unnecessary—would defeat the ladder’s simplicity. “Depends on bits”: incorrect; the beauty of the ladder is independence from N. Common Pitfalls: Confusing R–2R with binary-weighted DACs that need R, 2R, 4R, 8R, etc. Final Answer: two resistor values

Question 3

Terminology — what do we call processing an analog signal in the digital domain? Choose the correct term for the manipulation, analysis, and transformation of signals after converting them to numeric form and operating on them with algorithms.

Accepted Answer

Introduction / Context: Once an analog signal is sampled and quantized by an ADC, subsequent operations—filtering, detection, compression, spectral analysis—are performed numerically. The umbrella term for these algorithmic operations executed by processors, DSP cores, or FPGAs is Digital Signal Processing (DSP). Distinguishing DSP from conversion steps is foundational for system architecture. Given Data / Assumptions: Analog signals are first acquired via sensors and ADCs. Digital hardware or software performs computations on sampled data. Outputs may remain digital or be reconverted to analog via DACs. Concept / Approach: DSP includes time- and frequency-domain operations such as FIR/IIR filtering, FFT-based spectral estimation, adaptive filtering, and modulation/demodulation in software-defined radios. The key is that the manipulation happens numerically, not by analog components. ADC and DAC are conversion interfaces, not the manipulation itself, while “signal filtering” without context could be analog or digital. Step-by-Step Solution: Acquire analog → ADC → produce sampled numeric sequence.Apply algorithms (filters, transforms, detectors) in the digital domain.Optionally output via DAC or store/transmit digitally.This pipeline defines Digital Signal Processing. Verification / Alternative check: Standard textbooks define DSP as the analysis and manipulation of signals using digital techniques, distinct from the conversion steps themselves. Why Other Options Are Wrong: Analog-to-digital conversion: the front-end conversion, not the manipulation. Digital-to-analog conversion: output interface, not processing. Signal filtering: ambiguous; could be analog filtering as well. Amplitude modulation: a specific analog/digital communications technique, not the general term. Common Pitfalls: Equating “digital filter” with ADC; the filter is the processing after conversion. Final Answer: Digital signal processing

Question 4

Applications — where is the dual-slope ADC most widely used? Select the instrument class in which the dual-slope (integrating) analog-to-digital converter is extensively employed due to its noise rejection and accuracy characteristics.

Accepted Answer

Introduction / Context: Dual-slope (integrating) ADCs are renowned for excellent rejection of line-frequency interference and for stable, accurate measurements over modest speeds. These traits make them ideal for instrumentation where precision outweighs throughput. Identifying their common application helps match ADC type to system requirements. Given Data / Assumptions: Line-frequency noise (50/60 Hz) is a common contaminant in bench measurements. Measurement intervals can be synchronized to reject such interference. Throughput needs are low (few readings per second). Concept / Approach: In a dual-slope ADC, the input is integrated for a fixed time, then a reference of opposite polarity is integrated until the integrator output returns to zero. The time of the second phase is proportional to the input voltage. Averaging during integration inherently filters noise, especially if the fixed interval equals an integer number of line cycles. Digital voltmeters traditionally adopt this approach to maximize accuracy and noise immunity at low cost. Step-by-Step Solution: Map requirements: precision and noise rejection → dual-slope architecture.Identify instrument class with such needs → DVMs and multimeters.Other instruments (function generators, counters) do not prioritize integrating conversion.Therefore, the best match is digital voltmeters. Verification / Alternative check: Service manuals and block diagrams of classic bench DVMs show integrating ADCs precisely for their line interference rejection and stable calibration behavior. Why Other Options Are Wrong: Function generators: are signal sources, not precision measurement ADC users. Frequency counters: count edges with time bases; ADCs are tangential. All of the above: overly broad and incorrect. Audio DACs: unrelated; this is about ADCs, not DACs. Common Pitfalls: Confusing “integrating ADC” with digital filtering; the integrator is analog and precedes digitization. Final Answer: digital voltmeters

Question 5

Troubleshooting DACs — which performance characteristics are evaluated? When verifying a digital-to-analog converter (DAC), which of the following characteristics might be checked to assess linearity and gain/offset behavior?

Accepted Answer

Introduction / Context: Diagnosing DAC issues requires more than a quick output check. Linearity metrics such as differential nonlinearity (DNL), integral nonlinearity (INL), and monotonicity, along with gain and offset errors, determine how faithfully digital codes translate into analog levels. A systematic evaluation helps separate device faults from reference or load problems. Given Data / Assumptions: Nonmonotonicity refers to outputs that decrease for an increasing code or vice versa. DNL measures step-size deviation between adjacent codes. “Low and high gain” here summarizes gain and offset errors (slope and intercept). Concept / Approach: To test a DAC, sweep codes across the range and measure the output with a calibrated DMM or ADC. Compute step sizes to estimate DNL, fit a line to all points to extract gain (slope) and offset (intercept), and check for monotonic behavior. Excessive DNL (> 1 LSB) can cause nonmonotonicity; significant gain/offset error skews absolute accuracy even when linearity is acceptable. Step-by-Step Solution: Apply a stable reference and proper load within compliance limits.Sweep digital input codes and record analog output.Compute consecutive code differences → DNL estimation.Fit output vs. code → gain and offset error.Inspect monotonicity → ensure strictly increasing/decreasing as expected. Verification / Alternative check: Compare measured DNL/INL with datasheet limits. Use histogram or code-density tests to reveal hidden glitches or missing codes. Why Other Options Are Wrong: Any single metric alone can miss faults; comprehensive testing includes all listed characteristics. “Only output compliance voltage” is a boundary condition, not a linearity metric. Common Pitfalls: Ignoring reference stability and load impedance; both can masquerade as DAC faults. Final Answer: all of the above

Question 6

Understanding the flash (parallel) ADC principle The flash method of analog-to-digital conversion uses an array of comparators that compare reference voltages with the analog input voltage to generate a thermometer code.

Accepted Answer

Introduction / Context: Flash (parallel) ADCs are renowned for extremely high conversion speeds. They achieve this by using many comparators operating in parallel to compare the input against a ladder of reference voltages, producing a thermometer code that is then encoded into binary. Given Data / Assumptions: An N-bit flash ADC employs 2^N - 1 comparators. A resistor ladder generates evenly spaced reference levels. Digital encoding logic converts the thermometer pattern to binary output. Concept / Approach: Each comparator outputs 1 if Vin exceeds its reference and 0 otherwise. The boundary where outputs switch indicates the quantization level. Because all comparisons happen simultaneously, latency is only the comparator and encoder delay, making flash the fastest ADC architecture. Step-by-Step Solution: Build a reference ladder producing Vref*(k/2^N), for k = 1..2^N-1.Compare Vin to each reference using parallel comparators.Capture the thermometer code where ones transition to zeros.Encode this pattern into the N-bit digital output. Verification / Alternative check: Sweep Vin from 0 to Vref and verify monotonic step transitions at expected thresholds; check DNL/INL using measured transition points. Why Other Options Are Wrong: “Incorrect” contradicts the flash architecture. The other options refer to unrelated converter types or bit depths. Common Pitfalls: High power and large area due to many comparators; input kickback; encoder bubble errors requiring bubble-correction logic. Final Answer: Correct

Question 7

Throughput requirements in digital signal processing The statement “Digital signal processing must be at least half as fast as the incoming signal to be processed” is evaluated in the context of sampling theory and real-time processing constraints.

Accepted Answer

Introduction / Context: Real-time DSP systems must process sampled data fast enough to keep up with the stream without underflow or overflow. The Nyquist criterion states that the sampling rate must exceed twice the highest input frequency component to avoid aliasing, not that the processor can be half as fast as the signal. Given Data / Assumptions: The input is sampled at Fs samples per second, where Fs > 2*Fmax of the signal. The DSP must sustain an average throughput equal to or greater than Fs times operations per sample. Buffering is finite; steady-state throughput must meet or exceed input rate. Concept / Approach: Processing speed relates to operations per sample and sample rate, not the analog signal frequency directly. If 50 operations are required per sample and Fs is 1 MS/s, the processor must execute at least 50 Mops/s in real time. The “half as fast” statement confuses sampling criteria with processing throughput. Step-by-Step Solution: Determine the required sampling rate: Fs > 2*Fmax.Estimate operations/sample for the chosen algorithm.Compute required processing rate = Fs * operations/sample.Provision processing headroom for interrupts, I/O, and worst-case paths. Verification / Alternative check: Profile the implementation to confirm that average and worst-case cycle budgets meet real-time deadlines with safety margin. Why Other Options Are Wrong: “Correct” misstates Nyquist and ignores throughput. The other qualifiers do not rescue the incorrect blanket claim. Common Pitfalls: Confusing Nyquist with CPU speed; ignoring algorithmic complexity; neglecting I/O and DMA overheads. Final Answer: Incorrect

Question 8

Comparing digital and analog filtering performance The blanket statement “Digital filtering is faster than analog filtering” is assessed considering implementation details and application requirements.

Accepted Answer

Introduction / Context: Speed comparisons between digital and analog filters depend on the operating frequency, technology, and performance metrics. Analog filters operate continuously at the speed of the electronics, while digital filters process sampled data and are limited by ADC/DAC rates and computation throughput. Given Data / Assumptions: Analog filters require no sampling and no discrete-time computation. Digital filters require ADC and DAC (for mixed-signal systems) and enough processing power per sample. Metrics of “faster” vary: latency, bandwidth, settling time, or throughput. Concept / Approach: At very high RF or microwave frequencies, analog filters are typically the only practical option. Digital filters excel in flexibility, precision, and stability but can be constrained by conversion and processing limits. Therefore, the blanket claim of digital being faster is not generally valid. Step-by-Step Solution: Identify required bandwidth and dynamic range.Check feasibility of ADC/DAC at that bandwidth and latency.Estimate operations/sample for the digital filter; compare with available processing power.If digital constraints exceed capability, consider analog or hybrid approaches. Verification / Alternative check: Prototype both approaches or simulate to compare latency and throughput for the target specification. Why Other Options Are Wrong: “Correct” and the other options impose arbitrary boundaries; performance is application-dependent. Common Pitfalls: Ignoring converter bottlenecks; underestimating computational load; overlooking quantization noise and latency introduced by digital processing. Final Answer: Incorrect

Question 9

Fastest ADC architecture comparison Evaluate the statement: “The flash (parallel) converter is the fastest analog-to-digital conversion method used in practice.”

Accepted Answer

Introduction / Context: ADC architectures trade off speed, resolution, power, and area. Flash converters prioritize speed by performing all threshold comparisons in parallel, minimizing latency to only comparator and encoding delays. Given Data / Assumptions: Flash ADC uses 2^N - 1 comparators for N bits. Architectures like SAR, pipeline, and sigma-delta have sequential elements that add latency. We compare maximum achievable sampling rates at modest resolutions (commonly up to 8 bits for flash). Concept / Approach: Parallel operation eliminates per-bit decision cycles. While pipeline ADCs can be very fast at higher resolutions, pure latency and per-sample decision time remain lowest for flash designs, making them the fastest for moderate resolutions. Step-by-Step Solution: Assess comparator array and encoder delay paths.Compare with SAR (binary search per sample) and pipeline (stage-by-stage) latencies.Conclude that parallel comparison gives the shortest conversion time.Note practical limits: power and area grow exponentially with resolution. Verification / Alternative check: Survey datasheets: the highest per-channel sample rates at low-to-moderate resolution are flash or time-interleaved flash. Why Other Options Are Wrong: “Incorrect” ignores widespread practice. The other statements add conditions not required for the general conclusion. Common Pitfalls: Confusing maximum sample rate with resolution; overlooking power/area trade-offs; forgetting encoder bubble suppression. Final Answer: Correct

Question 10

DAC static performance terminology The statement “Offset is the characteristic of a DAC defined by the absence of any incorrect step reversals” is assessed against standard definitions of offset error and monotonicity.

Accepted Answer

Introduction / Context: Digital-to-analog converters are described using static performance terms such as offset error, gain error, differential nonlinearity (DNL), integral nonlinearity (INL), and monotonicity. Accurate terminology is important for interpreting datasheets and verifying performance. Given Data / Assumptions: Offset error refers to deviation of the actual transfer function from the ideal at zero code (or the first transition). Monotonicity means the output never decreases when the input code increases by one step; absence of step reversals defines monotonic behavior. DNL and INL quantify step size errors and cumulative deviation from ideal. Concept / Approach: The statement confuses offset and monotonicity. Offset is a vertical shift of the transfer curve; monotonicity is concerned with the direction of successive steps. A DAC can be perfectly monotonic yet have offset, and vice versa. Step-by-Step Solution: Define offset error: difference between actual and ideal output at a reference code (often zero code).Define monotonicity: output does not decrease for increasing code.Compare definitions with the statement; observe mismatch.Conclude the statement is incorrect; it describes monotonicity, not offset. Verification / Alternative check: Review any DAC datasheet glossary: offset and monotonicity are listed as distinct parameters. Why Other Options Are Wrong: “Correct” would propagate the definition error. The technology-specific options are irrelevant; the definitions are universal. Common Pitfalls: Mixing offset, gain error, and INL; assuming monotonicity implies small DNL (it does not necessarily). Final Answer: Incorrect

Question 11

Binary-weighted-input DAC resistor selection In a binary-weighted-input digital-to-analog converter, the values of the input resistors are deliberately chosen to be proportional to the binary weights of the corresponding input bits.

Accepted Answer

Introduction / Context: Binary-weighted DACs implement digital-to-analog conversion by summing scaled contributions from each bit. The most significant bit contributes half of full-scale, the next contributes one-quarter, and so on, requiring precise weighting in the hardware network. Given Data / Assumptions: Each bit controls a switch that connects a resistor (or current source) weighted by 2^n. Resistor values or current magnitudes follow binary ratios: 1, 1/2, 1/4, ... of full-scale. Ideal operation assumes perfectly matched components and switches. Concept / Approach: For a resistor-summing implementation, smaller resistance corresponds to larger contribution (higher conductance). Thus, resistor values are selected so conductance is proportional to the bit weight. Alternatively, current-steering versions use binary-weighted current sources. The common theme is binary proportionality to implement the sum. Step-by-Step Solution: Assign each bit a weight of 2^n relative to LSB.Choose resistor values so that conductance scales with 2^n (or equivalently resistance scales with 1/2^n).Sum node produces an analog voltage proportional to the digital code.Calibrate or trim if necessary to correct mismatch-induced errors. Verification / Alternative check: Simulate a ramp across all codes; verify monotonic steps and correct full-scale output based on resistor ratios. Why Other Options Are Wrong: “Incorrect” contradicts the fundamental structure of binary-weighted DACs. “Only true for R-2R” is wrong—the R-2R ladder is a different approach that avoids wide resistor spreads. “Valid only with current sources” ignores resistor-summing implementations. Common Pitfalls: Large resistor ratio spreads limit resolution; switch on-resistance and parasitics disturb exact weights; temperature coefficients degrade linearity. Final Answer: Correct

Question 12

Analog-to-Digital conversion methods: In practical digital signal processing systems, the successive-approximation (SAR) technique is widely used for A/D conversion due to its balance of speed, accuracy, and cost. Evaluate this statement.

Accepted Answer

Introduction / Context: SAR (successive-approximation register) analog-to-digital converters are prevalent in embedded, instrumentation, and industrial control applications. This question tests recognition of why SAR ADCs are considered the default choice in many mixed-signal designs. Given Data / Assumptions: SAR ADCs provide moderate-to-high resolution (typically 8–18 bits). They offer good sampling speeds (from tens of kS/s to several MS/s). Cost, power, and footprint must be reasonable for widespread use. Concept / Approach: A SAR ADC performs a binary search using a DAC, comparator, and control logic. At each comparison step, it narrows the error until the digital code matches the analog input within one LSB. This process achieves fast conversions without the complexity and power of flash ADCs and without the slower throughput of integrating (dual-slope) techniques. Step-by-Step Solution: Identify trade-offs across ADC families: flash (fast, power-hungry), pipeline (very fast, higher latency), integrating (accurate average, slower), delta-sigma (high resolution, limited bandwidth), SAR (balanced).Match common system needs (sensor interfaces, data acquisition) to SAR's strengths.Conclude that SAR is broadly adopted due to versatility. Verification / Alternative check: Survey typical microcontroller and data acquisition IC catalogs: most general-purpose ADC offerings are SAR, confirming market prevalence for mid-speed, mid-to-high resolution tasks. Why Other Options Are Wrong: Limiting SAR to low resolution, audio-only, or very low rates ignores modern SAR performance. The statement is not restricted to those cases. Common Pitfalls: Assuming “fastest” equals “most used.” Flash/pipeline may be faster but are not as broadly practical due to cost/power. Final Answer: Correct

Question 13

Digital-to-Analog converters: “Incorrect codes” are not an output error mode of a DAC; DAC output errors are measured as analog inaccuracies (offset, gain, DNL, INL), not digital code errors on the output.

Accepted Answer

Introduction / Context: A DAC accepts digital input codes and produces an analog output (voltage or current). This question checks conceptual clarity: where do “errors” manifest for a DAC—on a digital code line or as an analog deviation? Given Data / Assumptions: A DAC input is a digital code; its output is analog. Common DAC error metrics: offset error, gain error, differential nonlinearity (DNL), integral nonlinearity (INL), missing codes, and sometimes glitch impulse. “Incorrect codes” would imply an erroneous digital value on a DAC output, which does not exist because the DAC outputs an analog signal. Concept / Approach: DAC performance is evaluated by comparing the actual analog output to the ideal output for a given input code. Any discrepancy is an analog error. If an upstream digital block generates the wrong input code, that is a system-level digital error, not an intrinsic DAC “output error.” Step-by-Step Solution: Define DAC output: analog quantity driven by a digital input code.List analog-centric error terms (offset, gain, DNL, INL).Conclude “incorrect codes” describes a digital-input issue, not an analog-output error type. Verification / Alternative check: Check a DAC datasheet: error specifications are analog deviations (in LSB or percent of full scale), not “wrong digital codes” at the output. Why Other Options Are Wrong: Limiting to current-output DACs or ideal references misses the core idea. Load resistor affects accuracy but does not change the definition of error domains. Common Pitfalls: Confusing ADC “code errors” (digital output) with DAC, whose output domain is analog. Final Answer: Incorrect

Question 14

Digital signal processors (DSPs): A DSP must be programmed (via firmware or software) to perform specific signal-processing tasks such as filtering, FFT, or control algorithms. Assess this requirement.

Accepted Answer

Introduction / Context: A digital signal processor is a programmable engine designed to execute math-intensive operations efficiently. Unlike hardwired logic, a DSP requires code to define its behavior, making programmability central to its utility. Given Data / Assumptions: The DSP includes arithmetic units (MAC, multiplier, shifters) and specialized addressing modes. Applications vary widely: audio processing, communications, motor control, and sensing. Without firmware, the DSP does not perform a useful, application-specific function. Concept / Approach: Program code implements algorithms (IIR/FIR filters, FFT, PID control). The same silicon can be repurposed for different tasks simply by changing firmware, which is exactly why DSPs are powerful in diverse markets. Step-by-Step Solution: Identify the task (e.g., low-pass filter).Develop or import algorithm code optimized for the DSP architecture.Compile/flash the firmware and verify real-time behavior with test vectors. Verification / Alternative check: Demonstrations often swap DSP roles by reprogramming: the same chip can process speech one day and motor control the next, validating the programming requirement. Why Other Options Are Wrong: Fixed- vs floating-point does not change the need to program. An RTOS is optional. Hardware accelerators aid performance but still need top-level code to orchestrate data flow. Common Pitfalls: Assuming a DSP performs tasks “by default”—it must be instructed via software. Final Answer: Correct

Question 15

Device identification: The ADC0804 is a classic example of a successive-approximation (SAR) analog-to-digital converter. Determine whether this identification is accurate.

Accepted Answer

Introduction / Context: The ADC0804 family is widely cited in textbooks and labs as an 8-bit SAR ADC used for learning and simple data-acquisition tasks. This question checks familiarity with common ADC device types. Given Data / Assumptions: ADC0804 resolution: 8 bits. Architecture: successive-approximation using an internal DAC and comparator. Typical interfaces: simple parallel data outputs and start/ready control signals. Concept / Approach: SAR ADCs operate by iteratively comparing the input with a DAC-generated voltage, halving the search space each step until the N-bit code is resolved. ADC0804 implements exactly this procedure internally, which is why it is used in introductory mixed-signal experiments. Step-by-Step Solution: Recall the device family and datasheet classification.Match the described behavior (binary search via DAC + comparator) to SAR principles.Confirm: ADC0804 is a SAR ADC. Verification / Alternative check: Reference schematics show timing for start-of-conversion and end-of-conversion that align with SAR sequences, not integrating or delta-sigma methods. Why Other Options Are Wrong: Clock source and Vref details do not alter the converter architecture. Resolution is fixed at 8 bits, not 10. Common Pitfalls: Confusing ADC0804 (SAR) with integrating devices such as dual-slope ADCs used in DMMs. Final Answer: Correct

Question 16

GPIB (IEEE-488) control: The principal advantage of the three-wire handshake (DAV, NRFD, NDAC) is that it accommodates devices with different speeds on the same bus. Choose the best completion.

Accepted Answer

Introduction / Context: The GPIB (IEEE-488) interface is a classic parallel bus used to connect instruments and controllers. Its robust three-wire handshake ensures reliable data transfers among devices with varying processing speeds. Given Data / Assumptions: Handshake lines: DAV (Data Valid), NRFD (Not Ready For Data), NDAC (Not Data Accepted). Multiple devices (talkers/listeners) may have different internal latencies. Handshake controls data flow independent of raw cable speed. Concept / Approach: The talker asserts DAV when data are stable. Each listener deasserts NRFD once ready, and later deasserts NDAC when the data is accepted. This closed-loop process waits for the slowest listener before proceeding, allowing a mix of fast and slow devices without data corruption. Step-by-Step Solution: Talker places data on the bus.Listeners indicate readiness via NRFD.Talker asserts DAV; listeners latch data.Listeners assert NDAC low-to-high to acknowledge acceptance; bus advances to next byte. Verification / Alternative check: Observe timing diagrams: data moves forward only when all handshake conditions are satisfied, regardless of individual device speeds. Why Other Options Are Wrong: It is not inherently “faster”; it is robust and adaptable. Cable length is limited by spec, not enhanced by handshake. RS232C is a serial standard with different signaling. Common Pitfalls: Assuming parallel automatically means fastest throughput; bus speed is governed by the slowest accepted handshake. Final Answer: allows fast and slow listeners on the same bus

Question 17

DAC error behavior: An offset error appears as a constant shift of the output transfer curve and therefore causes an incorrect analog output for all input codes. Choose the correct statement.

Accepted Answer

Introduction / Context: Offset error in a DAC is a fundamental specification. It indicates that the entire transfer function is shifted up or down by a fixed amount relative to the ideal response. Given Data / Assumptions: DAC outputs analog values proportional to digital input codes. Offset error is defined as a constant deviation at zero or near-zero code and persists across the range. Gain error and linearity errors are separate phenomena. Concept / Approach: Because offset adds a constant term, y_actual = y_ideal + K, every code experiences the same shift K. Thus every input code is affected. Step-by-Step Solution: Model ideal transfer: y_ideal = m * code.Include offset: y_actual = m * code + K.Note that K does not depend on code; therefore all codes are shifted equally. Verification / Alternative check: Datasheets show offset as a single parameter added to the output regardless of the code, corroborated by calibration procedures that subtract a fixed value. Why Other Options Are Wrong: Limiting to high or low inputs contradicts the definition. “Scattered inputs” describes nonlinearity, not offset. Reference drift changes conditions but offset remains a constant additive error under fixed reference. Common Pitfalls: Confusing offset (constant) with gain error (slope) and DNL/INL (code-dependent). Final Answer: for all inputs

Question 18

Weighted-resistor DAC design: In a 4-bit weighted-resistor DAC, if the MSB input is applied to a 20 kΩ resistor, determine the resistor value used for the LSB branch (assume ideal binary weighting).

Accepted Answer

Introduction / Context: Weighted-resistor DACs implement binary weighting by assigning different resistor values to each bit input. The MSB has the largest weight (largest contribution), which corresponds to the smallest resistor. The LSB has the smallest weight and therefore the largest resistor. Given Data / Assumptions: 4-bit DAC with bits b3 (MSB), b2, b1, b0 (LSB). MSB branch uses 20 kΩ. Ideal binary weighting: each lower-significance bit contributes half the previous bit’s current. Concept / Approach: In a simple weighted-resistor summing DAC, branch current is inversely proportional to branch resistance for a common reference voltage: I ∝ 1/R. To achieve a binary current ratio, each successive bit’s resistor doubles relative to the previous, so that I_LSB = I_MSB / 8 in a 4-bit system (since 2^(4−1) = 8). Step-by-Step Solution: Given R_MSB = 20 kΩ for b3.Binary weighting requires R_b2 = 40 kΩ, R_b1 = 80 kΩ, R_b0 = 160 kΩ.Therefore, the LSB resistor is 160 kΩ. Verification / Alternative check: Check current ratios: I_MSB : I_b2 : I_b1 : I_LSB = 1 : 1/2 : 1/4 : 1/8, which meets binary weighting when resistors are 20 kΩ, 40 kΩ, 80 kΩ, 160 kΩ. Why Other Options Are Wrong: 2.5 kΩ and 5 kΩ would overweight the LSB. 80 kΩ is insufficient; it corresponds to b1. 320 kΩ would underweight the LSB beyond binary ratio. Common Pitfalls: Mixing up R-2R ladder (uniform R values) with weighted-resistor DACs (varying R values). Final Answer: 160 kΩ

Question 19

Bus interfacing: To emulate an open-collector output on a shared bus using a standard logic device, each output should drive a separate external transistor stage acting as the open collector.

Accepted Answer

Introduction / Context: Open-collector (or open-drain) outputs allow wired-AND (wired-OR with active-low logic) connections on a bus. If a logic IC lacks native open-collector outputs, designers can create the behavior with external components. Given Data / Assumptions: Need to share a bus line among multiple outputs safely. Require the ability for any device to pull the line low while a pull-up resistor returns it high. Standard push-pull outputs cannot be tied together safely. Concept / Approach: An external NPN transistor (collector to bus, emitter to ground) implements the low-side pull. The logic device drives the base (often via a resistor). A single pull-up resistor on the bus restores HIGH. This mirrors open-collector behavior and supports wired-AND signaling. Step-by-Step Solution: Add an NPN transistor per output line.Connect collector to the bus node; emitter to ground.Insert a base resistor from logic output to transistor base.Use a shared pull-up resistor from bus node to Vcc. Verification / Alternative check: Scope the bus: multiple outputs can assert LOW without contention, and the pull-up restores HIGH when all are inactive. Why Other Options Are Wrong: A series resistor does not prevent opposing drivers from fighting. LEDs and plain CMOS buffers do not provide open-collector behavior. Diode-ORing is not equivalent to open-collector with proper logic levels. Common Pitfalls: Forgetting proper base current limiting; omitting the bus pull-up; mixing push-pull and open-collector drivers on the same net. Final Answer: discrete transistor

Question 20

Comparing A/D methods: Among single-slope ramp, dual-slope ramp, successive-approximation, and tracking converters, which typically achieves the fastest conversions (ignoring flash/pipeline types)?

Accepted Answer

Introduction / Context: ADC architectures balance speed, accuracy, complexity, and power. Without considering flash or pipelined ADCs, designers often choose between ramp/integrating, tracking, and successive-approximation approaches for general-purpose conversions. Given Data / Assumptions: Single-slope and dual-slope integrate over time; these methods are comparatively slow but can be very accurate (dual-slope) and reject noise. Tracking converters adjust a running estimate one LSB at a time; worst-case convergence can be slow for large input changes. SAR converters complete in approximately N comparison steps for N-bit resolution. Concept / Approach: A SAR ADC resolves an N-bit code in about N comparator decisions regardless of the analog input value. In contrast, integrating methods require a full integration interval and reference run-down. Tracking may require many steps if the input changes significantly. Step-by-Step Solution: Compare per-conversion time: T_SAR ≈ N * t_cmp, typically short.Integrators: T ≈ controlled by fixed gate times → slower.Tracking: T depends on difference between current code and target → potentially longer. Verification / Alternative check: Datasheets for typical SAR ADCs show much higher sample rates than low-cost ramp or dual-slope designs. Why Other Options Are Wrong: Single- and dual-slope prioritize precision over speed. Tracking can lag large steps. Therefore, of the listed, SAR is typically fastest. Common Pitfalls: Equating “fastest possible ADC” with SAR; flash/pipeline are faster but were not options here. Final Answer: successive-approximation converter

Digital Signal Processing Questions