DigitHelm
Computer Science

Z-Transform Calculator | Transform Pairs, Properties & Inverse Z-Transform

Look up Z-transform pairs from a comprehensive table covering unit impulse, unit step, ramp, exponential, sine, cosine, and more. Apply linearity, time-shift, convolution, and differentiation properties. Compute the inverse Z-transform for causal rational polynomial fractions.

Instant Results100% FreeAny DeviceNo Sign-up

Z-Transform Pair Table

Sequence x[n]Z-Transform X(z)ROC
δ[n] (unit impulse)1All z
u[n] (unit step)z / (z − 1)|z| > 1
n·u[n] (ramp)z / (z − 1)²|z| > 1
n²·u[n]z(z + 1) / (z − 1)³|z| > 1
n³·u[n]z(z² + 4z + 1) / (z − 1)⁴|z| > 1
aⁿ·u[n]z / (z − a)|z| > |a|
n·aⁿ·u[n]az / (z − a)²|z| > |a|
n²·aⁿ·u[n]az(z + a) / (z − a)³|z| > |a|
−aⁿ·u[−n−1]z / (z − a)|z| < |a|
cos(ω₀n)·u[n]z(z − cos ω₀) / (z² − 2z·cos ω₀ + 1)|z| > 1
sin(ω₀n)·u[n]z·sin ω₀ / (z² − 2z·cos ω₀ + 1)|z| > 1
aⁿ·cos(ω₀n)·u[n]z(z − a·cos ω₀) / (z² − 2az·cos ω₀ + a²)|z| > |a|
aⁿ·sin(ω₀n)·u[n]az·sin ω₀ / (z² − 2az·cos ω₀ + a²)|z| > |a|
x[n − k] (time shift)z^(−k)·X(z)ROC of X(z)
nCr(n,k)·u[n]z^k / (z − 1)^(k+1)|z| > 1
δ[n − k]z^(−k)All z ≠ 0
u[n] − u[n − N](1 − z^(−N)) / (1 − z^(−1))|z| > 0
rⁿ·u[n − k]z^(−k)·z / (z − r)|z| > |r|
e^(−an)·u[n]z / (z − e^(−a))|z| > e^(−a)
1/n! (causal)e^(1/z)|z| > 0
aⁿ/n! (causal)e^(a/z)|z| > 0
x[−n] (time reversal)X(1/z)1/ROC

What Is the Z-Transform Calculator | Transform Pairs, Properties & Inverse Z-Transform?

The Z-transform converts a discrete-time sequence x[n] into a complex-frequency domain representation X(z), analogous to how the Laplace transform works for continuous-time signals. The Region of Convergence (ROC) specifies which values of z make the series converge. For causal signals (x[n] = 0 for n < 0), the ROC is the exterior of a circle in the complex z-plane. Z-transforms are foundational in digital signal processing, discrete control systems, and difference equation analysis. The inverse Z-transform via partial fractions decomposes a rational X(z) into simpler terms whose inverses can be read from standard tables.

Formula

X(z) = Σₙ₌₋∞^∞ x[n]·z^(−n)   |   Inverse: x[n] = (1/2πj) ∮ X(z)·z^(n−1) dz

How to Use

  1. 1

    Open the "Transform Table" tab and search for a sequence by keyword (e.g. "step", "cos", "ramp") or filter by category.

  2. 2

    Read the Z-transform X(z) and Region of Convergence (ROC) for your sequence from the table.

  3. 3

    Switch to "Properties" to see how linearity, time-shift, scaling, convolution, and time-reversal modify X(z).

  4. 4

    For the inverse Z-transform, switch to "Inverse Z-Transform" and enter numerator [n₀, n₁] and denominator [d₀, d₁, d₂] coefficients.

  5. 5

    Click a preset (e.g. "z/(z−0.5)(z−0.25)") or enter your own coefficients — the denominator must be degree 2 and numerator degree 1 or lower.

  6. 6

    Click "Compute Inverse" to see the pole locations, residues, and the causal inverse x[n] = Σ Aᵢ·pᵢⁿ·u[n].

  7. 7

    Read the x[n] sequence values for n = 0 through 7 displayed as individual tiles.

Use the three tabs: browse the searchable transform pair table, read property formulas, or compute the inverse Z-transform via partial fractions for a rational X(z).

Example Calculation

X(z) = z / (z² − 0.75z + 0.125) = z / (z − 0.5)(z − 0.25). Numerator: [0, 1] (i.e. z). Denominator: [0.125, −0.75, 1]. Poles: p₁ = 0.5, p₂ = 0.25. Residue A₁ = (0 + 1·0.5) / (1·(0.5 − 0.25)) = 0.5/0.25 = 2. Residue A₂ = (0 + 1·0.25) / (1·(0.25 − 0.5)) = 0.25/(−0.25) = −1. x[n] = 2·(0.5)ⁿ·u[n] − 1·(0.25)ⁿ·u[n]. Check: x[0] = 2−1 = 1, x[1] = 1 − 0.25 = 0.75.

Understanding Z-Transform | Transform Pairs, Properties & Inverse Z-Transform

Z-Transform Pairs Quick Reference

Sequence x[n]Z-Transform X(z)ROCNotes
δ[n]1All zUnit impulse
u[n]z/(z−1)|z|>1Unit step
n·u[n]z/(z−1)²|z|>1Unit ramp
aⁿ·u[n]z/(z−a)|z|>|a|Causal exponential
n·aⁿ·u[n]az/(z−a)²|z|>|a|Damped ramp
cos(ω₀n)·u[n]z(z−cosω₀)/(z²−2z·cosω₀+1)|z|>1Causal cosine
sin(ω₀n)·u[n]z·sinω₀/(z²−2z·cosω₀+1)|z|>1Causal sine
aⁿcos(ω₀n)·u[n]z(z−a·cosω₀)/(z²−2az·cosω₀+a²)|z|>|a|Damped cosine
δ[n−k]z^(−k)|z|>0Shifted impulse
u[n]−u[n−N](1−z^(−N))/(1−z^(−1))|z|>0Rectangular window

Z-Transform vs. Laplace Transform Correspondence

Continuous (Laplace)Discrete (Z-Transform)Relationship
F(s)X(z)z = e^(sT) for sampling period T
Unit impulse δ(t) → 1δ[n] → 1Both transforms of impulse = 1
Unit step 1/sz/(z−1)Poles at s=0 and z=1
Exponential e^(−at) → 1/(s+a)aⁿu[n] → z/(z−a)a = e^(−αT) discretization
Left-half plane stable |Re(s)|<0Unit disc stable |z|<1Stability region maps by z=e^(sT)
Imaginary axis jω (Fourier)Unit circle z=e^(jω) (DTFT)Stable boundary
Integration ÷ sAccumulation ÷ (1−z^(−1))Discrete integration

Z-Transform in Digital Signal Processing

  • Transfer function: For a digital filter described by Σ bₖ·x[n−k] = Σ aₖ·y[n−k], the transfer function H(z) = B(z)/A(z) where B and A are polynomials in z^(−1). Poles of H(z) determine stability; zeros determine frequency nulls.
  • FIR filters: Finite Impulse Response filters have all poles at z=0 (always stable). Their Z-transform is a polynomial in z^(−1).
  • IIR filters: Infinite Impulse Response filters have poles away from z=0. Butterworth, Chebyshev, and elliptic digital filters are designed by mapping analog prototypes via the bilinear transform z = (2/T)(1−z^(−1))/(1+z^(−1)).
  • Stability test: All poles inside the unit circle |pᵢ| < 1 is required for BIBO stability. The Jury stability test is the discrete analogue of the Routh-Hurwitz criterion.
  • Frequency response: H(e^(jω)) = H(z)|_{z=e^(jω)} gives the magnitude and phase response of a digital filter as a function of normalized frequency ω ∈ [0, π].

Frequently Asked Questions

What is the Region of Convergence (ROC)?

The ROC is the set of complex values z for which the Z-transform sum converges absolutely. For causal sequences (starting at n=0), the ROC is |z| > r₀ (outside a circle). For anti-causal sequences, the ROC is |z| < r₁ (inside a circle). For two-sided sequences, the ROC is an annulus. The ROC determines the type of system (stable, causal, anti-causal) and must be specified along with X(z) for uniqueness.

How does the Z-transform relate to the Discrete Fourier Transform (DFT)?

The DFT is the Z-transform evaluated on the unit circle: X(e^(jω)) = X(z)|_{z=e^(jω)}. This is only valid when the unit circle is in the ROC, i.e. the sequence is absolutely summable. The Z-transform generalizes the DFT to arbitrary complex z and is the discrete-time analogue of the Laplace transform.

When can partial fractions be used for the inverse Z-transform?

Partial fractions work when X(z) is a rational function N(z)/D(z) with degree(N) < degree(D). You expand X(z)/z = Σ Aᵢ/(z − pᵢ) for simple poles, multiply by z to get X(z) = Σ Aᵢz/(z − pᵢ), then use the standard pair aⁿ·u[n] ↔ z/(z−a). For repeated poles, include terms Aᵢₖ·z/(z−pᵢ)ᵏ.

What does the time-shift property Z{x[n-k]} = z^(-k)·X(z) mean in practice?

A delay of k samples in the time domain corresponds to multiplication by z^(−k) in the Z-domain. This makes Z-transforms ideal for analyzing difference equations y[n] = Σ aₖ·y[n−k] + Σ bₖ·x[n−k]: each delay operator becomes z^(−k), turning the difference equation into an algebraic equation in z. The transfer function H(z) = Y(z)/X(z) is then read directly.

What is a stable discrete-time system in terms of the Z-transform?

A causal LTI system with transfer function H(z) is BIBO stable (Bounded Input, Bounded Output) if and only if all poles of H(z) lie strictly inside the unit circle: |pᵢ| < 1 for all poles pᵢ. If any pole is outside or on the unit circle, the system is unstable (or marginally stable on the boundary). This is the discrete-time analogue of left-half-plane stability for continuous systems.

You Might Also Like

Explore 360+ Free Calculators

From math and science to finance and everyday life — all free, no account needed.