Skip to content

AND with NOR gates only

A AND B built from three NOR gates, NOR(NOR(A, A), NOR(B, B)), and checked against the plain AND. The equivalence holds on all four rows, and the engine's NOR converter produces exactly the same circuit.

A NOR with both inputs tied together is an inverter, and a NOR of two inverted signals is, by De Morgan, their AND: not (not A or not B) is A and B. The model carries A AND B as the reference and the three-NOR formula as the circuit. The engine's reading says conjunction is true exactly when both A and B are true; the table shows both columns true only on row 3, Σm(3).

The equivalence check holds: conjunction and with_nors agree on every assignment. The AND is not a tautology, counterexample A = FALSE, B = FALSE, and is satisfiable with the witness A = TRUE, B = TRUE. The simplifier found no law that shortens the single operator, and both normal-form converters return A AND B unchanged, with no law steps. The Karnaugh map draws one group on cell 3, one prime implicant. The NOR-only converter produces NOR(NOR(A, A), NOR(B, B)), 3 gates, which is character for character the hand-built circuit on this page; the NAND-only converter produces NAND(NAND(A, B), NAND(A, B)), also 3 gates.

This is the base case of NOR universality: with NOT and AND available, and OR from a single NOR plus an inverter, every function can be built. The counts matter in practice because a NOR family builds OR cheaply and AND expensively, while NAND does the reverse, and the comparison page tallies both for larger expressions. The thing to watch is the inverter cost: each tied-input NOR is a full gate, so three gates for one AND is the price of a single-gate-type library.

conjunction := A AND B · outputwith_nors := NOR(NOR(A, A), NOR(B, B)) · output

Exact checks

Always true (tautology)? No Counterexample: A = F, B = F · Exhaustively checked and solver cross-checked · 1 rows
Always false (contradiction)? No Counterexample: A = T, B = T · Exhaustively checked and solver cross-checked · 4 rows
Can be true? Yes Example assignment: A = T, B = T · Exhaustively checked and solver cross-checked · 4 rows
Are conjunction and with_nors equivalent? Yes · Exhaustively checked and solver cross-checked · 4 rows

Truth table (4 rows)

AND with NOR gates only
#ABconjunctionwith_nors
0FALSEFALSEFALSEFALSE
1FALSETRUEFALSEFALSE
2TRUEFALSEFALSEFALSE
3TRUETRUETRUETRUE

Plain-language reading

Beginnerconjunction is true exactly when both A and B are true.
In every other case, conjunction is false.
About the operators:
- AND is true only when all of its parts are true.
Intermediateconjunction is true exactly when both A and B are true.
In every other case, conjunction is false.
Advancedconjunction is true exactly when both A and B are true [conjunction ∧].
In every other case, conjunction is false.
Formal notation: A ∧ B

Logic diagram

AND with NOR gates only AND with NOR gates only A B conjunctionPRIMARY with_nors AND NOR NOR NOR
AND with NOR gates only AND with NOR gates only A B conjunctionPRIMARY with_nors AND NOR NOR NOR

Simplification, step by step

The engine found no law that shortens this expression; it is already as short as its rewriting laws can make it.

A AND B

Karnaugh map

A \ B
0
1
0
0m0
0m1
1
0m2
1m3
  1. cells m3A AND B
A AND B · Derived A AND B. Exactly equivalent on every assignment.

Minterms and maxterms

conjunction(A, B) = Σm(3)conjunction(A, B) = ΠM(0, 1, 2)

Variable order: A, B · 4 rows

Normal forms

Conjunctive normal form (CNF)

A AND B

2 clauses, 2 literals · complete

Disjunctive normal form (DNF)

A AND B

1 terms, 2 literals · complete

NAND-only and NOR-only

NAND-only form

NAND(NAND(A, B), NAND(A, B))

3 gates · verified equivalent on every row

NOR-only form

NOR(NOR(A, A), NOR(B, B))

3 gates · verified equivalent on every row

The same rule as code

Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).

Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.

Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

Inputs (dict keys, declared order) and generated parameter names:
    'A' -> input_A
    'B' -> input_B

Outputs (declared order) and their functions:
    'conjunction' -> output_conjunction(inputs)
    'with_nors' -> output_with_nors(inputs)

Assumptions (the declared scope, checked by in_scope(inputs)):
    none; every complete assignment is in scope.

Input contract:
    Every required input must be exactly True or False (type(value) is bool). Missing
    inputs, None, strings, integers (including 0 and 1), and keys that are not declared
    inputs raise InputError before anything is evaluated, including inputs that a
    short-circuiting operator would never read. Results are always bool.

Scope:
    Decision functions raise OutOfScopeError when the inputs violate an assumption; an
    out-of-scope assignment is never turned into a False result. Pass check_scope=False
    to evaluate without the scope check, and use in_scope(inputs) to test it separately.

Usage:

    import tangibool_model as model

    model.evaluate({'A': False, 'B': False})
    # -> {'conjunction': False, 'with_nors': False}
    model.output_conjunction({'A': False, 'B': False})
    # -> False
    model.evaluate({'A': 1, 'B': False})
    # raises InputError: 1 is an int, not a bool
"""

import collections.abc as _abc
from typing import Dict, Mapping, Tuple

__all__ = [
    "MODEL_INFO",
    "IDENTIFIER_MAP",
    "INPUT_NAMES",
    "OUTPUT_NAMES",
    "ASSUMPTIONS",
    "InputError",
    "OutOfScopeError",
    "minterm_index",
    "in_scope",
    "evaluate",
    "output_conjunction",
    "output_with_nors",
]

MODEL_INFO = {
    "format": "tangibool.python/1",
    "mode": "decision",
    "model": None,
    "revision": "reference",
    "interpretation": None,
    "exporter": "1.0.0",
    "engine_version": "1.0.0",
    "semantics_version": "tangibool-semantics/1",
    "logic_digest": "56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "conjunction",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_51ec16294c5a": {"kind": "input", "name": "A", "identifier": "input_A"},
    "v_15897b676c41": {"kind": "input", "name": "B", "identifier": "input_B"},
    "f_dd96c92ea782": {"kind": "output", "name": "conjunction", "identifier": "output_conjunction"},
    "f_d4997667f875": {"kind": "output", "name": "with_nors", "identifier": "output_with_nors"},
}

INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("conjunction", "with_nors")
ASSUMPTIONS = ()

_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}


class InputError(ValueError):
    """Raised when inputs are missing, undeclared, or not exactly True/False.

    Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
    integers (including 0 and 1), and keys that are not declared inputs are rejected before
    anything is evaluated. A missing input is never treated as False. The individual problems
    are listed in the problems attribute.
    """

    def __init__(self, problems):
        self.problems = tuple(problems)
        super().__init__("Invalid inputs: " + "; ".join(self.problems))


class OutOfScopeError(ValueError):
    """Raised when the inputs violate the model's assumptions (its declared scope).

    Outside its assumptions the rule has no decision: this error is never a False result.
    The names of the violated assumptions are in the violated attribute.
    """

    def __init__(self, violated):
        self.violated = tuple(violated)
        details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
        super().__init__(
            "Out of scope: the inputs violate "
            + ("assumption " if len(self.violated) == 1 else "assumptions ")
            + details
            + ". The rule has no decision outside its assumptions; this is not a False result."
        )


def _short_repr(value: object) -> str:
    text = repr(value)
    return text if len(text) <= 60 else text[:57] + "..."


def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
    """Validate inputs strictly before anything is evaluated and return a plain copy."""
    if type(check_scope) is not bool:
        raise TypeError("check_scope must be True or False")
    if not isinstance(inputs, _abc.Mapping):
        raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
    problems = []
    for key in inputs:
        if type(key) is not str or key not in _DECLARED_INPUTS:
            declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
            problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
    needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
    for name in needed:
        if name not in inputs:
            problems.append("missing required input " + repr(name))
    for key, value in inputs.items():
        if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
            problems.append(
                "input " + repr(key) + " must be True or False (type bool), got "
                + type(value).__name__ + " " + _short_repr(value)
            )
    if problems:
        raise InputError(list(dict.fromkeys(problems)))
    return dict(inputs.items())


def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
    problems = [
        "argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
        + type(value).__name__ + " " + _short_repr(value)
        for name, value in pairs
        if type(value) is not bool
    ]
    if problems:
        raise InputError(problems)


def minterm_index(*values: bool) -> int:
    """Row index of an assignment over a declared variable order.

    The first value is the most significant bit, so all-False is row 0. Used for indexed
    minterm (sum) and maxterm (product) forms.
    """
    index = 0
    for value in values:
        if type(value) is not bool:
            raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
        index = index * 2 + (1 if value else 0)
    return index


def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
    """Value of every enabled, confirmed assumption for validated inputs, in declared order."""
    return ()


def _require_in_scope(values: Dict[str, bool]) -> None:
    violated = tuple(name for name, holds in _scope_values(values) if not holds)
    if violated:
        raise OutOfScopeError(violated)


def in_scope(inputs: Mapping[str, bool]) -> bool:
    """True when the inputs satisfy every assumption (the model's declared scope).

    Requires the inputs the assumptions read; raises InputError for invalid inputs.
    """
    values = _checked_inputs(inputs, (), True)
    return all(holds for _, holds in _scope_values(values))


# ---- Formula bodies (inputs are validated by the public functions below) ----


def _compute_output_conjunction(input_A: bool, input_B: bool) -> bool:
    """Decision output 'conjunction': A AND B"""
    return input_A and input_B


def _compute_output_with_nors(input_A: bool, input_B: bool) -> bool:
    """Decision output 'with_nors': NOR(NOR(A, A), NOR(B, B))"""
    return not ((not (input_A or input_A)) or (not (input_B or input_B)))


# ---- Public API ----


def output_conjunction(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'conjunction' (stable ID f_dd96c92ea782).

    'conjunction' := A AND B

    Required inputs: 'A', 'B'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("A", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_conjunction_value = _compute_output_conjunction(values["A"], values["B"])
    return output_conjunction_value


def output_with_nors(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'with_nors' (stable ID f_d4997667f875).

    'with_nors' := NOR(NOR(A, A), NOR(B, B))

    Required inputs: 'A', 'B'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("A", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_with_nors_value = _compute_output_with_nors(values["A"], values["B"])
    return output_with_nors_value


def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
    """Evaluate every output (declared order); each output is computed once.

    Required inputs: 'A', 'B'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("A", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_conjunction_value = _compute_output_conjunction(values["A"], values["B"])
    output_with_nors_value = _compute_output_with_nors(values["A"], values["B"])
    return {
        "conjunction": output_conjunction_value,
        "with_nors": output_with_nors_value,
    }
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
--   v_51ec16294c5a: A -> "in_A"
--   v_15897b676c41: B -> "in_B"
-- Output columns:
--   f_dd96c92ea782: conjunction -> "out_conjunction"
--   f_d4997667f875: with_nors -> "out_with_nors"
-- Assumptions (scope K; exported separately and never folded into an output):
--   none; every complete input row is in scope.

-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_conjunction] output conjunction (stable ID f_dd96c92ea782)
--   conjunction := A AND B
--   PRECONDITION: "in_A", "in_B" are non-NULL booleans.
--   ("in_A" AND "in_B")
--
-- [out_with_nors] output with_nors (stable ID f_d4997667f875)
--   with_nors := NOR(NOR(A, A), NOR(B, B))
--   PRECONDITION: "in_A", "in_B" are non-NULL booleans.
--   (NOT ((NOT ("in_A" OR "in_A")) OR (NOT ("in_B" OR "in_B"))))
--

-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_A", "in_B") AS (
  -- tangibool:example-rows:begin
  VALUES
    (1, FALSE, FALSE),
    (2, FALSE, TRUE),
    (3, TRUE, FALSE),
    (4, TRUE, TRUE),
    (5, NULL::boolean, FALSE)
  -- tangibool:example-rows:end
),
checked AS (
  SELECT
    inputs.*,
    CASE
      WHEN "in_A" IS NULL OR "in_B" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_A",
  "in_B",
  "status",
  CASE WHEN "status" = 'ok' THEN ("in_A" AND "in_B") END AS "out_conjunction",
  CASE WHEN "status" = 'ok' THEN (NOT ((NOT ("in_A" OR "in_A")) OR (NOT ("in_B" OR "in_B")))) END AS "out_with_nors"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1)
; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo.
; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build).
; Model: not stated
; Source revision: reference
; Interpretation status: not stated
; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
; Logic digest: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b
; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
; Mode: decision
;
; Reading results: each query block states what sat and unsat mean for its question. A sat
; answer comes with a witness or counterexample (ask for it with (get-model) after sat only);
; unsat settles that scoped query and is solver-checked, not an independently checked proof
; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never
; accumulate across questions. This checks the supplied model, not whether it is a complete,
; legal, or safe statement of a real-world rule.
;
; Symbols (stable ID: name -> symbol):
;   v_51ec16294c5a: A -> in_A
;   v_15897b676c41: B -> in_B
;   f_dd96c92ea782: conjunction -> out_conjunction
;   f_d4997667f875: with_nors -> out_with_nors

(set-option :produce-models true)
(set-info :smt-lib-version 2.6)
(set-logic QF_UF)

; ---- Inputs (declared order)
(declare-const in_A Bool) ; A
(declare-const in_B Bool) ; B

; ---- Outputs (dependencies first)
; output conjunction (stable ID f_dd96c92ea782): A AND B
(define-fun out_conjunction () Bool (and in_A in_B))
; output with_nors (stable ID f_d4997667f875): NOR(NOR(A, A), NOR(B, B))
(define-fun out_with_nors () Bool (not (or (not (or in_A in_A)) (not (or in_B in_B)))))

; ---- Assumptions (the scope K; never folded into an output)
; K: conjunction of the enabled, confirmed assumptions (true when there are none)
(define-fun K () Bool true)

; ---- Definitions only: nothing is asserted and no check is run.
; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
conjunction = A \cdot B
with_nors = \overline{\overline{A + A} + \overline{B + B}}

The common slip

Using NOR(A, B) as an AND because it is false on three rows and true on one. It is true on the wrong row, A = FALSE, B = FALSE; AND is true on A = TRUE, B = TRUE. The inverters on the inputs are what move the single true row from 0 to 3, as the table shows.

Questions

Why is the NAND form of AND also 3 gates?

NAND(A, B) is already NOT AND, so one more NAND wired as an inverter restores the AND, and the converter counts the two gates of the inverter stage with the first: NAND(NAND(A, B), NAND(A, B)). Both families need 3 for this function, for different reasons.

Related

Operators and gates

Tools

Practice

See also

Worked examples

Generated from the Tangibool engine, semantics tangibool-semantics/1, engine 1.0.0, build . Every table, map, simplification and verdict on these pages was computed by the Tangibool engine when the site was built, and every claim was re-checked by the same exact checker the app uses. Limits are stated where they apply. © 2026 Foreranger LTD.